The aim of an objective image quality assessment is to find an automatic algorithm that evaluates the quality of pictures or video as a human observer would do. To reach this goal, researchers try to simulate the Human Visual System (HVS). Visual attention is a main feature of the HVS, but few studies have been done on using it in image quality assessment. In this work, we investigate the use of the visual attention information in their final pooling step. The rationale of this choice is that an artefact is likely more annoying in a salient region than in other areas. To shed light on this point, a quality assessment campaign has been conducted during which eye movements have been recorded. The results show that applying the visual attention to image quality assessment is not trivial, even with the ground truth.

The temporal distortions such as flickering, jerkiness and mosquito noise play a fundamental part in video quality assessment. A temporal distortion is commonly defined as the temporal evolution, or fluctuation, of the spatial distortion on a particular area which corresponds to the image of a specific object in the scene. Perception of spatial distortions over time can be largely modified by their temporal changes, such as increase or decrease in the distortions, or as periodic changes in the distortions. In this work, we have designed a perceptual full reference video quality assessment metric by focusing on the temporal evolutions of the spatial distortions. As the perception of the temporal distortions is closely linked to the visual attention mechanisms, we have chosen to first evaluate the temporal distortion at eye fixation level. In this short-term temporal pooling, the video sequence is divided into spatio-temporal segments in which the spatio-temporal distortions are evaluated, resulting in spatio-temporal distortion maps. Afterwards, the global quality score of the whole video sequence is obtained by the longterm temporal pooling in which the spatio-temporal maps are spatially and temporally pooled. Consistent improvement over objective existing video quality assessment methods is observed. Our validation has been realized with a dataset built from video sequences of various contents.

Image quality assessment have been extensively studied during this past few decades. It is obviously very important to provide a mean to judge an image's quality without having to ask to human observers for a subjective image quality evaluation. Many computer softwares have been build in this aim. This is called objective quality assessment. Such metrics are usually of three kinds, they may be Full Reference (FR), Reduced Reference (RR) or No Reference (NR) metrics. We focus here on a new technique which recently appeared in quality assessment metrics: data-hiding-based image quality metric. Regarding the amount of data to be transmitted for quality assessment purpose, this latter is placed in between RR and NR metrics. A little overhead due to the embedded watermark is added to the image. A perceptually weighted watermark is embedded into the host image, and an evaluation of this watermark leads to assess the host image's quality. In such context, the watermark robustness is crucial. The watermark must resist to most attacks, but it must also be degraded along with the image distortion. Our work is compared to existing metrics in terms of the correlation (et de RMSE ?) with subjective assessment and in terms of data overhead induced by the mark. * http://www.its.bldrdoc.gov/vqeg/projects/rrnr-tv/RRNR-tv _draft_v1_7g.doc † The combinaison is : Q(α) = α * M hf + (1 − α) * M mf , with α lies between 0 and 1.

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Most of the efficient objective image or video quality metrics are based on properties and models of the Human Visual System (HVS). This paper is dealing with two major drawbacks related to HVS properties used in such metrics applied in the DWT domain : subband decomposition and masking effect. The multi-channel behavior of the HVS can be emulated applying a perceptual subband decomposition. Ideally, this can be performed in the Fourier domain but it requires too much computation cost for many applications. Spatial transform such as DWT is a good alternative to reduce computation effort but the correspondence between the perceptual subbands and the usual wavelet ones is not straightforward. Advantages and limitations of the DWT are discussed, and compared with models based on a DFT. Visual masking is a sensitive issue. Several models exist in literature. Simplest models can only predict visibility threshold for very simple cue while for natural images one should consider more complex approaches such as entropy masking. The main issue relies on finding a revealing measure of the surround influences and an adaptation: should we use the spatial activity, the entropy, the type of texture, etc.? In this paper, different visual masking models using DWT are discussed and compared.

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In order to verify the identity of a cardholder user, the typing of a PIN code is usually required, but this method does not guarantee the verification result. Only biometrics is able to authenticate an user as this information is strongly related to the user. To ensure security and privacy issues (such as the protection of the biometric data), Match On Card (MOC) solutions have been proposed. This approach consists in storing the biometric user's reference and computing the verification decision in a Secure Element (SE). The purpose of this paper is to propose an evaluation platform on biometric MOC for testing its performance and security. This platform allows to perform tests given scenarios and benchmarks for comparing MOCs. We illustrate the usefulness of this platform on a commercial MOC.