Mateugue Diack

Tégaye Diop

Ramatoulaye Ndiaye

Restoration of Degraded Lands Affected by Salinization Process under Climate Change Conditions: Impacts on Food Security in the Senegal River Valley

Keywords: Climate change, Agriculture, Degraded land, Salinization, Food security

With the progressive degradation of agricultural lands due to climate change, there is a need to document how land responds to management practices and their resilience to salinization processes. Salinization is a problem associated with agriculture and it constitutes a constraint which results in inappropriate practices. In addition, agriculture intensification and changes in temperature and precipitation patterns expected from climate change are likely to affect the salt-water balance of fragile ecosystems. Information on the relationship between climate change and salt-affected land salinization processes is scattered. Little has been done to highlight the most affected or vulnerable areas or to promote practices that can be used to adapt agricultural production in fragile areas to climate change. This study will contribute to food security and reducing the stress on ecosystems. This study relates land management practices to lands degraded and salinized due to climate change. Several land management practices have been used to evaluate the level of restoration of degraded lands. Changes in temperature, relative humidity, evapora-tion, solar radiation, and soil temperature combined with agricultural management practices driven by different cropping systems were used to evaluate the best and most adapted management practices for degraded lands that are dedicated to agriculture. Results suggest that regular seasonal cropping under irrigation might be recommended for degraded lands to assure sustainable food security.

Introduction

Land degradation due to salinization has revealed negative interactions that have been developing for decades from an increase in population and a decrease of crop production in developing countries. This has aggravated inequalities in food production across regions [START_REF] Parry | Climate change, global food supply and risk of hunger[END_REF]. Moreover, food insecurity is likely to increase with climate change unless early warning systems and development programs are used more effectively [START_REF] Brown | Food security under climate change[END_REF]. Developing countries in sub-Saharan Africa are more vulnerable because of the dominance of agriculture in their economies, the scarcity of capital for adaptation measures, warmer baseline climates, and heightened exposure to extreme events (Tubiello andFischer 2007, 2013;[START_REF] Brown | Food security under climate change[END_REF]. Soils affected by salinization processes have become a major environmental issue. Nearly 800 million hectares of land worldwide, more than 6 % of the total surface area of lands, are affected by salinization processes. In addition, climate changes aggravate losses of arable lands in the arid and semi-arid zones [START_REF] Boivin | De ´sertification et salinisation des terres au Se ´ne ´gal: proble `mes et reme `des[END_REF]. In Senegal, estimated total lands affected by salinization processes are 1,000,000 ha to 1,700,000 ha [START_REF] Gobin | Indicators for pan-European assessment and monitoring of soil erosion by water[END_REF][START_REF] Sadio | Pe ´doge `nese et potentialite ´s forestie `res des sols sulfate ´s acides sale ´s des tannes du Sine Saloum, Se ´ne ´gal[END_REF]. More recent studies conducted by the National Institute of Pedology in Senegal ( 2008) have estimated the total land affected by salinization processes at 996,950 ha (Fig. 1). The same studies underlined that the level of salinization requires setting up strategy control the phenomenon and determining the origin or the source of the salt within the considered soil. Those studies did not identify the arable surface area affected by The salinization process or indicate the sources of specific salinization processes for any surface area listed. Salinization processes are near to irreversible in the case of heavy-textured soils with high levels of swelling clay [START_REF] Marius | Effets de la se ´cheresse sur l'e ´volution des mangroves du Se ´ne ´gal et de la Gambie[END_REF]Sadio 1986). The nature of the salt, the plant species, and even the individuality of the plant (e.g., structure and depth of the root system) determine the concentration of soil-salt levels at which a crop or plants will succumb (Photo.1). Consequently, there has been a progres-sive reduction of crop production regardless of whether it is for industrial or feeding purposes [START_REF] Diop | Les techniques de de ´salinisation des terres de la Compagnie Sucrie `re Se ´ne ´galaise: impact sur l'environnement[END_REF]. Therefore, improving and maintaining croplands to improve crop productivity are critical when techniques are adapted to restore lands affected by salinization processes [START_REF] Marius | Contribution a ` l'e ´tude des mangroves du Se ´ne ´gal et de Gambie. Ecologiepe ´dologie-ge ´ochimie. Mise en valeur et ame ´nagement (Re ´sume ´ de the `se)[END_REF]. Although a combination of efficient drainage and flushing of the soil by water is often used, the leaching of salts from the profile is rarely effective [START_REF] Boivin | Les sols argileux de la re ´gion de Nianga-Podor: re ´partition, caracte ´ristiques, aptitude et risques de de ´gradation sous irrigation[END_REF][START_REF] Boivin | Les sols de la moyenne valle ´e du fleuve Se ´ne ´gal: caracte ´ristiques et e ´volution sous irrigation[END_REF]. Because the reclamation, improvement, and management of salt affected soils necessitate complex and expensive technologies, all efforts must be taken for the efficient prevention of these harmful processes [START_REF] Lal | Soil management in the developing countries[END_REF]. Adequate soil and water conservation practices based on a comprehensive soil or land degradation assessment can become an early warning system that provides possibilities for efficient salinity (or alkalinity) control, the prevention of these environmental stresses, and their undesirable ecological, economic, and social consequences (Le [START_REF] Brusq | Evolution de la salinite ´ des sols et des eaux en relation avec la riziculture submerge ´e dans le delta du fleuve Se ´ne ´gal (campagne 1981-1982)[END_REF]. As adaptation practices refer to actual adjustments or changes in decision environments, which might ultimately enhance resilience or reduce vulnerability to observed or expected changes in climate, a changing climate will result in considerable changes in natural vegetation and in land use practices [START_REF] Adger | Assessment of adaptation practices, options, constraints and capacity[END_REF]). These changes in turn result in a feedback effect on the climate, which considerably affects the field water cycle and soil formation/degradation processes [START_REF] Lal | Soil processes and water quality[END_REF][START_REF] Lal | Soil degradation: I. Basic processes[END_REF][START_REF] Va ´rallyay | Potential impacts of climate change on agro-ecosystems[END_REF][START_REF] Harnos | Climate change: environment-risk-society[END_REF][START_REF] Va ´rallyay | Impact of climate change on soils in Hungary[END_REF]. The integral influence of climate, hydrology, and vegetation land use changes are reflected by the fiel water balance and soil moisture regimen [START_REF] Va ´rallyay | Fertilizer -liquid manure -drinking water relationships[END_REF][START_REF] Va ´rallyay | Climate change, soil salinity and alkalinity[END_REF][START_REF] Va ´rallyay | Symposium and 3rd training course 'Climate change: challenge for training of applied plant scientist. Impact of climate change on crop production[END_REF][START_REF] Farkas | A chernozem soil water regime response to predicted climate change scenarios[END_REF]. Climate change and its hydrological consequences may result in the significant modification of soil conditions (Va ´rallyay 2010). Changes in temperature, precipitation, natural vegetation, and land use practices will result in significant changes in crop production. In addition, climatic changes exacerbate the loss of woody lands in the arid and semi-arid regions [START_REF] Zeng | Atmospheric science. Drought in the Sahel[END_REF]. Soil salinity is also a serious problem in areas where the groundwater of high salt content is used for irrigation. The most serious salinity problems are faced by the irrigated arid and semi-arid regions of the world, and it is in these very dry regions that irrigation is essential to increase agricultural production to satisfy food requirements. The problem of soil degradation is a serious threat to the welfare of mankind. Although degradation of the land has always characterized man's systematic use of it, the process has accelerated in recent decades, precisely at a time when population growth and rising expectations have begun to demand enormous increases in food production. The issue is of overwhelming urgency. As the soil is subject to degradation, the cost of reclaiming it becomes higher, rising sharply until the threshold beyond which reclamation is no longer economically feasible is passed. Nearly 50 % of the irrigated land in the arid and semi-arid regions has some soil salinization problems. It is generally agreed that the future food needs of the increasing population will be met by directing the efforts of all concerned stakeholders (Le Gal 2001). D'aquino-Passouant (1995) studied the salinization process of the soils and noticed essentially two issues: the drainage of water and the rising of salt by capillarity. He revealed that the first process depended mostly on the nature and type of soil, as percolation of the vertisols and alfisols varied from 1 to 3 mm/day. Rising water by capillarity was due to the presence of shallow and high in salt groundwater that probably occurred during different periods of sea transgressions and regressions 2,000 years (before JC). This left a high quantity of soluble salt that remained at the fossil state in the soil of the delta, which explains the origin of the saline groundwater [START_REF] Ceuppens | Impact of non-drained irrigated rice cropping on soil salinization in the Senegal River Delta[END_REF]. Cropping systems are also responsible for rapid degradation of the soils of the delta as well as most of the abandoned private farms [START_REF] Breman | L'intensification agricole au Sahel[END_REF][START_REF] Dunia | Suivi de la salinisation dans les pe ´rime `tres irrigue ´s par te ´le ´de ´tection et syste `me d'information Ge ´ographique[END_REF]). [START_REF] Loyer | Degradation saline des sols induite par l'irrigation en domaine sahelien[END_REF] underlined that a converted system for irrigation without drainage and rice cultivation under permanent flooded conditions helps the desalinization process first for the soils and then for the groundwater. This induced and unexpected alkalinization of these soils due to a rise in the groundwater in which movements are not well controlled. This raises the issue of evacuating drainage water. [START_REF] Sadio | Pe ´doge `nese et potentialite ´s forestie `res des sols sulfate ´s acides sale ´s des tannes du Sine Saloum, Se ´ne ´gal[END_REF] described a similar process with the tanns of Sine Saloum, which are characterized by a remarkable heterogeneity due to their morphology and physical and chemical properties. Their characteristics are closely related to topography, type of material, and hydrology [START_REF] Sy | [END_REF]. Since 1971, their pedogenetic evolution has led to a severe drought all over the country, particularly in the fluviomarin fields [START_REF] Michel | Les bassins des fleuves Se ´ne ´gal et Gambie. Etude ge ´ographique[END_REF]. This has led to dramatic consequences for the social and ecological environment [START_REF]Enjeux de l'apre `s barrage valle ´e du Se ´ne[END_REF]. Moreover, insufficient rainfall induced by the aridness of the climate has triggered and amplified salt concentration and acidification of the soils all over the area. This salt concentration, which was spread out a little bit few decades ago, has quickly reached all soils, from the terrace up to the linked Photo. 2 a rice field under optimal management practices Colluviums. Data collected from the surveys conducted in several villages along the arm of the river showed that according to farmers, rice cultivation was the best management practice for restoring degraded salt affected lands because of its rooting system, its capacity to grow under flooded conditions, and its ability to be a salt tolerant crop (Photo.2). However, irrigation is often costly and technically complex, and it requires skilled management. Failure to apply efficient principles of water management may result in the wastage of water through seepage; over-watering and inadequate drainage create waterlogging and salinity problems that reduce the soil productivity, eventually leading to loss of cultivable land. The problems of salt-affected soils are old, but their magnitude and their intensity have been increasing fast due to large-scale efforts to irrigate additional areas in recent decades. The problems have been made worse by scaling up the irrigation systems without adequate provision for drainage, and they are being aggravated by poor water management practices and unsound reclamation procedures. The general characteristics and basic principles involved in the identification, reclamation, and management of salt-affected soils are the same throughout the world. In the delta of the Senegal River where the study took place, the salinity of the soils is a real issue with an anthropic origin. This issue touches the low and middle delta of the River Valley. The objective of this study was to (1) analyze the dynamics of salinization process as related to climate change and (2) compare different management practices to remediate saline soils for rice cropping.

Materials and Methods

The site was located in the upper limit of the delta of the Senegal River Valley. Over 900 ha of land with different soil orders identified as Inceptisol (18 %), Vertisol (36 %), Alfisol (14 %), and Aridisol (30 %) were severely affected by the salinization process (Soil Survey Staff 1992). To restore the degraded soils, different management practices were compared to determine the most efficient one that can be readily adopted by farmers. They consisted of (1) abandoning lands with no watering and no cropping (T1); (2) leaching followed by rice cropping (T2);

(3) leaching followed by continuous rice cropping (T3); (4) leaching followed by crop rotation with rice, sugarcane, and fallow (T4); and (5) leaching followed by continuous rice cropping over years (T5). The leaching process consisted of watering the plots with an amount of water that dissolved the accumulated salts in the soil to make them move downward to the root zone or shallowly wash the soil from the salt. The leaching process was the same as for all management practices except for the "abandoned plot." Plots receiving leaching treatments were arranged in bands of 0.7 ha separated by small flood banks. The dimensions of the bands differed from one plot to another. This way of turning plots into smaller bands ensured good coverage of the soil with a film of water and thus established the correct homogeneity of the plot. The watering by band consisted of having each band receive water up to a 40 cm height. In general, the frequency of watering was once every 25-30 days. After watering twice, the plot was dried out for 10 days before soil sampling at 0-20 and 20-30 cm for analysis in the laboratory. Once the soil sampling was done and the experimental design was set up, water was added to the plots. Each band was watered up to a 40 cm height. Watering was done while controlling the level of the water table with piezometers installed in the plots. This management could not proceed without knowing the physical and chemical characteristics of the soils. The idea was then to proceed with watering in a way that had one part of the salt percolate downward to be collected by the buried drains; the other part rose up by capillarity towards the shallow zone of the soil and was drained out towards the open drains. During leaching, water was sampled regularly from the plots, the buried drains, the open drains, and from irrigation. Changes in soil salinity concentration were measured by electrical conductivity (EC) at a 1:5 soil to water ratio. Likewise, soil acidity was measured by soil pH water at a 1:5 soil to water ratio.

Results and Discussion

In the delta of the Senegal River, salinization of the lands is one of the biggest constraints farmers are facing. This salinization process results in two phases that are characteristic of the evolution of the environment during the Nouakchotian transgression and the drought periods of the 1970-1980. During that time, marine transgression polluted the groundwater of the delta. Afterward, these waters became highly concentrated due to the fossilization of the salt. Indeed, the weakness of the flow strongly penalized traditional farming in the flooding zones as well as breeding in the falling areas. Moreover, such process favored the rising of the salty water in the bed of the river in more than hundred kilometers from the mouth of the river [START_REF] Guillaumie | La se ´cheresse au Sahel, un exemple de changement climatique[END_REF]. This rising action led to the construction of the Diama dam in 1986 to stop the saline intrusion and develop cropping systems under irrigation. However, if water softening is a major factor in the delta, the dam is causing the reverse process in zones in the drainage basins and in certain agricultural racks where irrigation practices have no drainage systems and are not well conducted. In the delta, itinerant agriculture reduces the potential for tenure, mainly in the Boudoum zone and Lake Guiers. From a social point of view, this work will cause collective awakening of the impact of these techniques on the environment and therefore on the socio-economic activities of the populations living around the lake. Through this awakening, they should reach self-sufficiency in rice and vegetable cropping in the Dieri upland zone. The physical factors influencing the salinization processes are rainfall, temperature, relative humidity, wind speed, solar radiation, total evaporation, and population. Therefore, a monitoring process was set up and run from 1997 to 2010. During the same period, mean annual temperatures were above 25 ! C. There were extreme temperatures from 26.2 ! C in 1999 to 29.1 ! C in 2007 (Fig. 3). An analysis of these data revealed that temperatures were higher than that usually recorded. Maximum temperatures were above 34 ! C, and the range was significant.

During that period, temperatures ranged from 14.5 to 17! C. The relative humidity was strongly influenced by the continental location and marine bangs (Ciss 2002). Analysis of the data over the last 12 years showed high Observations of all parameters seem to indicate that the period of sunshine determines evaporation. Indeed, the highest evaporation data were related to the maximum number of hours of sunshine, and this occurred during the years with high evaporation demand. This is the case for 2004 (Figs.5 and .6). With less rainfall and high temperatures over the period between 2005 and 2010, these 1972-1973 and 1984-1985. Thus, changes in the sedimentary regimen in the climatic context of the Sahelian zone raised fossil salts through thermocapillarity. Such salinity levels may be favored by the shallow depth of the groundwater, which is usually in contact with the plant roots due to inadequate irrigation and a maladapted drainage system. The amount of water added to better leach a saline rooting zone depends mainly on the initial level of salinity of the soil, the soil texture, and the leaching technique used. Nevertheless, findings from the work done by [START_REF] Charollais | Contribution a ` l'e ´tude du bilan hydrique de la canne a ` sucre dans les pe ´rime `tres irrigue ´s de la CSS, me ´moire de maitrise[END_REF] did not agree with previous diagnoses. They indicated a significant decrease in the (EC) at the surface and at depths for the cultivated soil, without significant changes in exchangeable Sodium percentage (ESP). This decrease in EC is illustrated by the changes in chlorides and sodium in solutions. In conjunction with this, the level of exchangeable sodium did not change significantly, but a significant decrease of the exchangeable potassium was noticed. This decrease in EC was also observed in the delta of Senegal. [START_REF] Wopereis | Soil fertility management in irrigated rice systems in the Sahel and Savanna regions and West Africa. Part 1: agronomic analysis[END_REF] confirmed that rice cultivation, whether it was drained or not, led to a desalination of the shallow layers. On irrigated rice, the presence of film water at the surface, with lower concentration, made a lateral evacuation of the salts possible along with the blocking the capillary increase in the saline in saline water and deep groundwater. In the fields alternating between cropping under dry conditions and rice cultivation, the crops grown for diversification, such as tomatoes or onions, were largely grown in irrigated fields for the villages or for private use, and most of these fields did not have a drainage system. The crops grown for diversification are mainly grown in the village's irrigated perimeters or in private perimeters, most of which do not have drainage systems. In spite of their filtering soils and unsuitability for rice cultivation-according to the extension agents and agents from development projects-farmers sometimes grow rice in these types of soils. This will allow for a leaching of the accumulated salts after few years of vegetable cropping. Before planting the crop, salt leaching was essentially done in the shallow water film and showed the effect of a flooding and evacuation cycle on changes in salinity of the surface horizon. Leaching by flooding showed that it was possible to obtain remarkable results. These results were encouraging and were obtained using a very rigorous soil and water management system for irrigation and drainage. By combining irrigation and cropping, convenient trends in the desalinization process were obtained. The set of management practices used produced different results with an overall decrease in salinity levels.

The abandoned plot treatment (Fig. 7) showed the highest salt content of 7.12 and 8.82 dS cm "1 at the soil surface (0-20 cm) and at the subsoil level (20-30 cm), respectively. Non-cultivated soils have a salinity level with chlorine and are totally saturated with sodium at deeper levels and are equally split into sodium, calcium, and magnesium at the surface. The first prospection [START_REF] Poitevin | Etude d'impact des techniques culturales sur les ame ´nagements hydro-agricoles dans la re ´gion de Podor (Se ´ne ´gal), me ´moire de quatrie `me anne ´e de l'ESAP, multigr. ORSTOM Dakar, p 53 et annexes Sadio S[END_REF] showed an increase in the electric conductivity (EC) in the irrigated fields in the studied zone. With the other management practices where irrigation, leaching, and cropping are combined, a gradual but significant decrease in soil salinity content was noticed (Fig. 7). At the shallow Fig. 7 Changes in electrical conductivity between management practices level (0-20 cm), the salinity level measured by EC 1/5 showed a decrease from 3.54 to 0.17 dS cm "1 in the four other management practices that went from leaching followed by rice cropping (T2) to rice cropping in the regular season (T5).

The mean concentration of the salinity in the plots under leaching with different croppings varied from 0.28 to 3.54.00 dS cm "1 for the 0-20 cm horizon (Fig. 7). For the 20-30 cm layer also, for all plots, a noticeable decrease in salt concentration was observed (3.24-0.17 dS cm "1 ) except for the abandoned plot (T1) where there was a saline rise through capillarity and an increase in salt concentration due to intense evaporation. At the shallow soil depth, this decrease was estimated to be 47.26 % between T1 and T2, 45.48 % between T2 and T3, 81.37 % between T3 and T4, and 6.67 % between T4 and T5. A comparison of these results with those from previous studies showed a noticeable decrease in the level of salinity. At a greater depth, the soil salinity decrease was estimated to be 63. 27 % between T1 and T2, 74.30 % between T2 and T3, 44.57 % between T3 and T4, and 84.78 % between T4 and T5. The study showed that it is possible to leach soluble salts initially present in saline profile up to 70 % by using an amount of water equivalent to 1/3 the thickness of the soil. This improves if the immersion continues with enough drainage water [START_REF] Hoffman | Guidelines for reclamation of salt-affected soils[END_REF]. Data have showed that with rice cultivation over years and a good drainage system, desalinization of the land can be achieved.

Leaching combined with different cropping systems, such as management practices to restore degraded soils affected by salinization processes, showed that from the soil surface to a shallow depth, soil acidity was close to neutral with a pH range of 6.75-7.27 (Fig. 8). At a greater depth (20-30 cm), data showed acidic soil. At any rate, soil pH at both depth levels presented optimal conditions for cropping after leaching processes were conducted.

Conclusion

Desalinization techniques used for 2 years showed convincing results not only for restoring degraded soils for better cropping but also for improving the socioeconomic environment through an increase of agricultural land tenure for food security. The positive impact of leaching saline soils was noticed in the gradual increase in arable land and thus potential for crop production and improvement in soil quality. Once recovered from salinization, these lands can represent the most suitable zones for cropping compared to the uplands. Globally, desalinization through rice cultivation presents an economic and ecological cost, which should be taken into account when it comes to restoring degraded salty soils for crop production.

Fig. 1

 1 Fig.1 Distribution of the affected zones by salinization processes (Source: National Institute of Pedology 2008)

Fig. 2

 2 Fig. 2 Changes in mean annual rainfall over time (Source: CSS Weather Station 2011)

Figure . 2

 2 shows that the geographical location of the delta in the Sahelian region has two seasons with contrasting rainfall patterns in the range of 200-400 mm. There is a short rainy season of 2-3 months and a dry season of 9-10 months.

Fig. 3 Fig. 4

 34 Fig. 3 Changes in annual mean temperature over time (Source: CSS Weather Station 2011)

Fig. 5 Fig. 6

 56 Fig. 5 Changes in total evaporation over time (Source: CSS Weather Station 2011)

Fig. 8

 8 Fig. 8 Changes in soil pH between management practices


Christophe Charrier
email: christophe.charrier@unicaen.fr

Abdelhakim Saadane
email: abdelhakim.saadane@univ-nantes.fr

Christine Fernandez-Maloigne

Blind Image Quality Assessment based on the use of Saliency Maps and a Multivariate Gaussian Distribution

Keywords: NR-IQA, saliency maps, MVGD

With the widespread use of image processing technologies, objective image quality metrics are a fundamental and challenging problem. In this paper, we present a new No-Reference Image Quality Assessment (NR-IQA) algorithm based on visual attention modeling and a multivariate Gaussian distribution to predict the final quality score from the extracted features. Computational modeling of visual attention is performed to compute saliency maps at three resolution levels. At each level, distortions of the input image are extracted and weighted by the saliency maps in order to highlight degradations of visually attracting regions. The generated features are used by a probabilistic model to predict the final quality score. Experimental results demonstrate the effectiveness of the metric and show better performance when compared to well known NR-IQA algorithms.

Introduction

The development of image and video processing technologies and the exponential increase of new multimedia services raise the critical issue of assessing the visual quality. From several years, a number of investigations have been conducted to design robust Image Quality Assessment (IQA) metrics. Such metrics aim at predicting image quality that well correlates with Mean Opinion Scores (MOS). No-Reference IQA (NR-IQA) are interesting as they assume no knowledge of the reference image and can be embedded in practical and real-time applications. Three approaches may be used in the design of IQA algorithms. The first one looks to mimic the behavior of the Human Visual System (HVS). The HVS models used in this context,include relevant properties such as the contrast sensitivity function, masking effects and detection mechanisms. A number of investigations [START_REF] Babu | An hvs-based no-reference perceptual quality assessment of JPEG coded images using neural networks[END_REF] have shown that these models when included in IQA algorithms, improve their performance. The second approach is well suited for assessing the quality of images distorted by known distortions. The algorithms of this approach quantify one or more distortions such as blockiness [START_REF] Muijs | A no-reference blocking artifact measure for adaptive video processing[END_REF][START_REF] Song | A new no-reference assessmet metric of blocking artefacts on HVS masking effect[END_REF], blur [START_REF] Parvezsazzad | No-reference image quality assessment for jpeg2000 based on spatial features[END_REF][START_REF] Barland | Blind quality metric using a perceptual map for jpeg2000 compressed images[END_REF] or ringing [START_REF] Hantao | A no-reference metric for perceived ringing artifacts in images[END_REF][START_REF] Hu | Jpeg ringing artifact visibility evaluation[END_REF] and score the image accordingly. The third and last approach is a general-purpose method. It considers that the HVS is very sensitive to structural information in the image and any loss of this structural information results in a perceptual loss of quality. To quantify loss of information, this approach uses Natural Scene Statistics (NSS). Generally, NSS-based algorithms apply a combination of learning-based approach with NSS-based extracted features. When a large ground truth is available, statistical modeling algorithms can achieve good performance. However, there is still an effort to provide to reach the subjective consistency of the HVS.

The work proposed in this paper is motivated by the interesting results of IQA when visual attention models are used. Computational visual saliency models extract regions that can attract human gaze. These regions are of a great interest in IQA. This paper presents a new NR-IQA based on the use of saliency maps to better weight the extracted distortions and combines these weighted distortions using a MultiVariate Gaussian distribution (MVGD). Fig. 1 presents the overall synopsis of the multi-scale proposed approach, namely SABIQ (SAliency-based Blind Image Quality) index. First, a multi-scale decomposition is performed on the input image and a saliency map is computed at each level. The base level corresponds to the first image while the remaining ones are obtained by a low-pass filtering followed by a sub-sampling. Secondly, different distortion maps are generated at same scale levels. At each level, the Renyi entropy of the subsampled image is also computed. Thirdly, for each level, a weighting of each computed distortion map with the corresponding saliency map is performed in order to increase the strength of degradation in visually attracting areas. Finally, the combination at each level of the weighted distortion map with the computed Renyi entropy is performed to design a multiresolution distortion map.

The proposed approach

The final stage of the pipeline is a simple Bayesian model that predicts the quality score of the input image. The Bayesian approach maximizes the probability that the image has a certain quality score given the features extracted from the image. The associate posterior probability is modeled as a MultiVariate Gaussian Distribution (MVGD).

Visual saliency map

Visual attention is the ability of the HVS to rapidly direct our gaze towards regions of interest in our visual environment. Two attentional mechanisms are involved in such selection; bottom-up and top-down. Main features known to influence bottom-up attention include color, orientation, motion. Top-down attention is rather driven by the observer's experience, task and expectation. Many conducted investigations have helped in understanding visual attention and many computational saliency models have been proposed in the literature [START_REF] Koch | Shifts in selective visual attention: Towards the underlying neural circuitry[END_REF][START_REF] Itti | A model of saliency-based visual attention for rapid scene analysis[END_REF].A recent state-of-the-art in visual attention is given in [START_REF] Borji | State-of-the-art in visual attention modeling[END_REF]. Most of these models use bottom-up approach and are based on the Feature Integration Theory of Treisman and Gelade [START_REF] Treisman | A feature-integration theory of attention[END_REF]. They compute a 2D map that highlights locations where fixations are likely to occur. These image-based (stimulus-driven) models use the same architecture but vary in the selection of characteristics used to calculate the global saliency map. The saliency models have addressed various applications, including computer vision [START_REF] Navalpakkam | An integrated model of top-down and bottom-up attention for optimizing detection speed[END_REF], robotics [START_REF] Breazeal | A context-dependent attention system for a social robot[END_REF] and visual signal processing [START_REF] Christopoulos | The jpeg2000 still image coding system: an overview[END_REF][START_REF] Walther | Selective visual attention enables learning and recognition of multiple objects in cluttered scenes[END_REF]. In the context of IQA algorithms, the saliency models are intended to extract the most relevant visual features that when combined, produce a quality score highly correlated with human judgment [START_REF] Ben Amor | Improved performance of quality metrics using saliency map and csf filter for standard coding h264/avc[END_REF].

Many research have been investigated to model the phenomenon that any human viewer can focus on attractive points at a first glance, and many saliency models have been proposed in the literature.

Saliency models can be categorized into 1) pixel-based models and 2) objectbased models. The pixel-based models aim to highlight pixel locations where fixations are likely to occur. The object-based models focus on detecting salient objects in a visual scene. The majority of saliency models in the literature are pixel-based saliency models, such as ITTI [START_REF] Itti | A model of saliency-based visual attention for rapid scene analysis[END_REF], STB [START_REF] Walther | Modeling attention to salient protoobjects[END_REF], PQFT [START_REF] Guo | A novel multiresolution spatiotemporal saliency detection model and its applications in image and video compression[END_REF], etc.

In this paper, the ITTI model [START_REF] Itti | A model of saliency-based visual attention for rapid scene analysis[END_REF] has been employed. This model combines multiscale image features into a single topographical saliency map. Three channels (Intensity, Color and Orientation) are used as low level features. First, feature maps are calculated for each channel via center-surround differences operation. Three kinds of conspicuity maps are then obtained by across-scale combination. The final saliency map is built through combining all of the conspicuity maps.

Distortion maps

Many studies have shown that image quality degradations are well measured by features of local structure [START_REF] Wang | Image quality assessment: From error visibility to structural similarity[END_REF], contrast [START_REF] Xue | Blind image quality assessment using joint statistics of gradient magnitude and Laplacian features[END_REF][START_REF] Wang | Image quality assessment: From error visibility to structural similarity[END_REF], multi-scale and multi-orientation decomposition [START_REF] Zhang | No-reference image quality assessment based on logderivative statistics of natural scenes[END_REF].

Contrast distortion map

The image gradient is an interesting descriptor to capture both local image structure and local contrast [START_REF] Zhang | A feature-enriched completely blind image quality evaluator[END_REF]. Also according to this study, the partial derivatives and gradient magnitudes change with the strength of applied distortions.

Following this strategy and in order to generate the contrast distortion map, we compute both horizontal and vertical gradient component images ∂I/∂x and ∂I/∂y from the image I. From those two gradient images, the gradient magnitude image is computed as (∂I/∂x) 2 + (∂I/∂y) 2 and then modelled by a Weibull distribution. This distribution fits well the gradient magnitude of natural images [START_REF] Scholte | Brain responses strongly correlate with weibull image statistics when processing natural images[END_REF] and its 2 parameters (the scale parameter and the shape parameter) roughly approximate the local contrast and the texture activity in the gradient magnitude map, respectively. Larger values of the scale parameter imply greater local contrast.

Yet, instead of computing the contrast on the entire image, the image is first partitioned into equally sized n × n blocks (referred to as local image patches), then the local contrast is computed for each block yielding in final to a local contrast map M C .

Structural distortion map

The structural distortion map considered here uses structural distortion features that are extracted from both spatial and frequency information. To extract image structure information from frequency domain, the image is partitioned into equally sized n × n local image patches and then a 2D-DCT (Discrete Cosine Transform) is applied on each patch. The feature extraction is thus locally performed in the spatio-frequency domain according to local spatial visual processing property of the HVS [START_REF] Blake | Perception. McGraw-Hill Higher Education[END_REF]. To capture degradation depending on directional information in the image, block DCT coefficients are modeled along three orientations (0, 45 and 90 degrees). For each orientation, a Generalized Gaussian is fitted to the associated coefficients, and the coefficient ζ is computed from the histogram model as ζ = σ(X)/µ(X) where σ(X) and µ(X) are the standard deviation and the mean of the DCT coefficient magnitudes, respectively. In order to select the most significant map from the three generated distortion maps, the variance of ζ is then computed for each orientation. The distortion map associated to the highest value of the variance of ζ is finally chosen and serve as structural distortion map, namely M S .

Since the DC (Direct Coefficient) does not convey any structural information, it is removed from all computations. multi-orientation image property map It is widely admitted that the HVS is sensitive to spatial frequency and orientation. In order to capture this sensitivity, the steerable pyramid transform [START_REF] Simoncelli | The steerable pyramid: a flexible architecture for multi-scale derivative computation[END_REF] is used.

Let a(i, j, f, θ) be an original coefficient issued from the decomposition process located at the position (i, j) in the frequency band f and orientation band θ. The associated squared and normalized coefficient r(i, j, f, θ) is defined as:

r(i, j, f, θ) = k a(i, j, f, θ) 2 φ∈[0,45,90,135] a(i, j, f, φ) 2 + σ 2 (1)
In this paper, four orientation bands with bandwidths of 45 degrees 0, 45, 90, 135 plus one isotropic lowpass filter are used yielding in five response maps {R θ , R iso }, θ ∈ [0, 45, 90, 135]. The distortion map associated to the highest value of the variance is finally selected and will serve as frequency variation distortion map, namely M F .

From the four orientation bands, we compute the energy ratio in order to take account the modification of local spectral signatures of an image. This approach is inspired from the quality BLIINDS2 index [START_REF] Saad | Blind image quality assessment: A natural scene statistics approach in the dct domain[END_REF]. Each map associated to θ {R θ }, θ ∈ [0, 45, 90, 135] is decomposed into equally sized n × n blocks. For each obtained patch, the average energy in frequency band θ models the variance corresponding to band θ as e θ = σ 2 θ . For each θ ∈ [45, 90, 135], the relative distribution of energies in lower and higher bands is then computed as:

E θ = |e θ -1/n t<θ e t | |e θ + 1/n t<θ e t | (2)
where 1/n t<θ e t represents the average energy up to frequency band θ. Three distortion maps are then generated. The distortion map associated to the highest value of the variance of E θ is finally selected and serves as energy ratio distortion map, namely M E .

Multiscale features computation

In this block, each distortion map is combined with the saliency map in order to obtain a saliency-based distortion map. From each saliency-based distortion map, a pooling strategy is applied by averaging over the highest 10th percentile coefficients across the distortion map. This pooling strategy is motivated by the fact that the "worst" distortions in an image heavily influence subjective impressions and that they are concentrated in few coefficients having higher values [START_REF] Moorthy | Visual importance pooling for image quality assessment[END_REF]. All the obtained values are referred to as df 10 (•), where (•) represents one of the computed distortion maps {M C , M S , M F , M E }. In order to get information about the distribution of the distortions (over space or isolated distortions), the 100th percentile average of the local scores is also computed. The obtained values are referred to as df 100 (•), where (•) represents one of the computed distortion maps {M C , M S , M F , M E }. The whole computation leads, in total, to 8 distortion features {df 10

(k), df 100 (k)}, ∀k ∈ {M C , M S , M F , M E }.
The final feature is computed at each scale level l as

final-feature p l (k) = df p l (k) * entropy l (3)
where p ∈ {10, 100}, k ∈ {M C , M S , M F , M E }, df p l (k) represents the value of the distortion value df p (k) at level l, and entropy l is the Renyi entropy of the associated saliency-based distortion map. This strategy yields us to include information about the anisotropy property of distortion maps. In this paper, the number of scales l is set to 3 as this value achieves the best performance.

Probabilistic Model and Quality score prediction

The computed features and the DMOS (Difference of Mean Opinion Scores) values of training images are then used by the learning block to fit a MVGD. The resulting model SABIQ is given by:

SABIQ (x) = 1 (2π) k/2 |Σ| 1/2 exp - 1 2 (x -β) T Σ -1 (x -β) (4)
where x = ({final-feature p l (k)}, DM OS) corresponds to the extracted features (Eq. 3) to which is added DMOS. β and Σ denote the mean and covariance matrix of the MVGD model and are estimated using the maximum likelihood method. The features extracted from testing images with DMOS values lying between 0 and 100 with a step of 0.5, are fed into the learned SABIQ to assess quality of image under test.

Performance evaluation

Apparatus

To provide comparison of NR-IQA algorithms, two publicly available databases are used: 1) TID2013 database [START_REF] Ponomarenko | Color image database for evaluation of image quality metrics[END_REF] and 2) CSIQ database [START_REF] Larson | Most apparent distortion: full-reference image quality assessment and the role of strategy[END_REF]. Since LIVE database [START_REF]LIVE Image Quality Assessment Database[END_REF] has been used to train both the proposed metric and most of the trail NR-IQA schemes, it has not been used to evaluate performances. To train our model, we used LIVE database running multiple train-test sequences. For each sequence, the image database is divided into distinct training and test sets. In each train-test sequence, 80% of the LIVE IQA Database content was chosen to design the training set, and the remaining 20% were dedicated to the test set. This means each training set contains 23 reference images and their associated distorted images.The quality scores are computed using a boostrap process with 999 replicates.

To assess the performance of SABIQ, the Spearman Rank Order Correlation Coefficient (SROCC) is computed between DMOS values and predicted scores from six state-of-the-art opinion-aware NR-IQA methods, including BRISQUE [START_REF] Mittal | No-reference image quality assessment in the spatial domain[END_REF], BLIINDS2 [START_REF] Saad | Blind image quality assessment: A natural scene statistics approach in the dct domain[END_REF], DIIVINE [START_REF] Moorthy | Blind image quality assessment: From natural scene statistics to perceptual quality[END_REF], CORNIA [START_REF] Mittal | No-reference image quality assessment in the spatial domain[END_REF], ILNIQE [START_REF] Zhang | A feature-enriched completely blind image quality evaluator[END_REF] and SSEQ [START_REF] Liu | No-reference image quality assessment based on spatial and spectral entropies[END_REF] which are all so far widely accepted in the research community.

Performance Evaluation

The SROCC between predicted DMOS and subjective DMOS is reported in Table 1 for the TID2013 database. From Table 1, one observes that SABIQ performs much better than the six other NR-IQA methods when the SROCC values for the whole database is considered. This significant gain in performance is likely induced by the visual attention that is used in the weighting of distortions. When single distortions are considered, SABIQ achieves performance comparable with CORNIA and performs better than the five remaining trail quality schemes. For multiple distortions, SABIQ performs better than BRISQUE, BLIINDS2, DIIVINE and SSEQ and competes very well with CORNIA and ILNIQE.

Similar results are shown in Table 2 for CSIQ Images database. SABIQ achieves better results for 4 out of 6 distortions and outperforms all trail NR-IQA algorithms when the entire database is considered. In this case, the gain in performance is about 7% compared to ILNIQE and is at least 32% compared to other metrics.

We also trained the methods on TID2013 excluding multi-distorted subsets (MD), then tested them on the two other datasets and the remaining MD subsets of TID2013. The results are shown in Table 3. The NR-IQA methods IL-NIQE and SABIQ clearly outperform the other trial method when trained on single distortion.When considering the LIVE database, IL-NIQE and SABIQ achieve almost the same results, which is not surprising since many existing recent NR-IQA schemes reach high correlations on that database. Furthermore, SABIQ presents the highest SROCC value with CSIQ database. All these results tend to highlight a high generalization capability of the proposed approach.

Conclusion

In this paper, we investigated how the visual attention property of the HVS can be embedded in the NR-IQA algorithm design and in which way it can improve the prediction of image quality. The proposed approach, namely SABIQ, is based on the use of computational modeling of visual attention to compute the saliency map. At each of the three levels of the multiresolution scheme, distortions of the input image are generated and weighted by the saliency maps in order to highlight degradations of visually attracting regions. The extracted features are used by a probabilistic model to predict the final quality score. The obtained results demonstrate the effectiveness of the approach.

Fig. 1 .

 1 Fig. 1. Overall synopsis of the multi-scale proposed approach

Table 1 .

 1 SROCC values of NR-IQA models on each distortion types for the TID2013 database.

		TID2013 subset	BRISQUE BLIINDS2 DIIVINE	SSEQ	ILNIQE CORNIA	SABIQ
		Additive Gaussian Noise	0.852	0.722	0.855	0.807	0.876	0.756	0.881
		Add Noise in Color Components	0.709	0.649	0.712	0.681	0.815	0.749	0.817
		Spatially Correlated Noise	0.491	0.767	0.463	0.635	0.923	0.727	0.933
		Masked Noise	0.575	0.512	0.675	0.565	0.512	0.726	0.726
		High Frequency Noise	0.753	0.824	0.878	0.860	0.868	0.796	0.910
	Single Distortion	Impulse Noise Quantization Noise Gaussian Blur Image Denoising JPEG Compression JPEG2000 Compression JPEG Transmission Errors	0.630 0.798 0.813 0.586 0.852 0.893 0.315	0.650 0.781 0.855 0.711 0.864 0.898 0.117	0.806 0.165 0.834 0.723 0.629 0.853 0.239	0.749 0.468 0.858 0.783 0.825 0.885 0.354	0.755 0.873 0.814 0.750 0.834 0.857 0.282	0.767 0.016 0.921 0.832 0.874 0.901 0.686	0.805 0.851 0.861 0.793 0.902 0.901 0.351
		JPEG2000 Transmission Errors	0.360	0.620	0.060	0.561	0.524	0.678	0.622
		Non Eccentricity Pattern Noise	0.145	0.096	0.060	0.011	0.080	0.286	0.102
		Local Block-wise Distortions	0.224	0.209	0.093	0.016	0.135	0.218	0.220
		Mean Shift	0.124	0.128	0.010	0.108	0.184	0.065	0.179
		Contrast Change	0.040	0.150	0.460	0.204	0.014	0.182	0.508
		Change of Color Saturation	0.109	0.017	0.068	0.074	0.162	0.081	0.182
	Multiple Dist.	Multiplicative Gaussian Noise Comfort Noise Lossy Comp. of Noisy Images Color Quant. with Dither Chromatic Aberrations Sparse Sampling and Reconst.	0.724 0.008 0.685 0.764 0.616 0.784	0.716 0.017 0.719 0.736 0.539 0.816	0.787 0.116 0.633 0.436 0.661 0.834	0.679 0.033 0.610 0.528 0.688 0.895	0.693 0.359 0.828 0.748 0.679 0.865	0.644 0.534 0.862 0.272 0.792 0.862	0.765 0.379 0.838 0.810 0.762 0.920
		Cumulative subsets	0.367	0.393	0.355	0.332	0.494	0.429	0.567

Table 2 .

 2 SROCC values of NR-IQA models on each distortion types for the CSIQ Images database.

	CSIQ subset	BRISQUE BLIINDS2 DIIVINE	SSEQ	ILNIQE CORNIA	SABIQ
	JP2K	0.866	0.895	0.830	0.848	0.906	0.746	0.920
	JPEG	0.903	0.901	0.799	0.865	0.899	0.908	0.941
	Gaussian Noise	0.252	0.379	0.176	0.872	0.850	0.914	0.870
	Add. Gaussian Pink Noise	0.925	0.801	0.866	0.046	0.874	0.420	0.895
	Gaussian Blur	0.903	0.891	0.871	0.873	0.858	0.917	0.920
	Global Contrast Decrement	0.029	0.012	0.396	0.200	0.501	0.302	0.509
	Cumulative subsets	0.566	0.577	0.596	0.528	0.815	0.663	0.877

Table 3 .

 3 SROCC values when trained on TID2013, excluding multi-distortion subsets (MD)

		LIVE CSIQ MD TID2013 subsets
	BRISQUE 0.522 0.639	0.122
	BLIINDS2 0.511 0.456	0.322
	DIIVINE 0.410 0.701	0.409
	SSEQ	0.230 0.630	0.098
	ILNIQE	0.899 0.631	0.568
	CORNIA 0.399 0.656	0.412
	SABIQ	0.896 0.787	0.562

This research is supported by the ANR project #ANR-16-CE39-0013.