Abdelhakim Saadane

Interactions of chromatic components in the perceptual quantization of the achromatic component

In the human color vision, it is well admitted that signals issued from the three types of receptors (L, M, S) are combined in two opponent color components and one achromatic component.In this paper, we are concerned by the cardinal directions A, Cr1 and Cr2 defined by Krauskopf. We study in particular the interactions between luminance and chromatic components. These interactions should be taken into account in visual coding since they modify the visibility thresholds. We present here results that show the influence of the two chromatic components on the optimal perceptual quantizer of the achromatic component in particular subbands. On the subband called III-1 of luminance, we show influence of Cr1 and Cr2 sinusoidal maskers. Other results are also presented on the subband called II-1 with Cr1 and Cr2 maskers.

Psychovisual approach for watermarking

The work presented here deals with watermarking algorithms. The goal is to show how the Human Visual System (H.V.S) properties can be taken into account in the conception of such algorithms. The construction of the watermarking algorithm presented in this paper needs three steps. In the first one the selection of auspicious sites for the watermark embedding is described. The selection exploits a multi-channel model of the Human Visual System which decomposes the visual input into seventeen perceptual components. Medium and high frequencies are then selected to generate a sites map. This latter is improved by considering some high level uniform areas. The second step deals with the choice of the strength to apply to the selected sites. The strength is determined by considering the H.V.S. sensitivity to the local band limited contrast. In the third step, examples of spatial watermarking embedding and extraction are given. The same perceptual mask has been successfully used in other studies. The watermark results from a binary pseudo-random sequence, of length 64, which is circularly shifted so as to occupy all the sites mentioned above. The watermark extraction exploits the detection theory and requires both the perceptual mask and the original watermark. The extracted watermark is then compared to the original and a normalized correlation coefficient is computed. This coefficient value allows the detection of the copyright.

Design and Evaluation of an Entirely Psychovisual-Based Coding Scheme

In this paper a new psychovisual-based coding scheme is proposed. The analysis and the quantization stages, the two main functions which determine the performances of a coding scheme, are based on the human visual system properties. In the first stage, a filter bank decomposes images into subimages of perceptual significance when a contrast transformation is applied. Analytic cortex filters have been used because they provide an accurate modelization of visual receptive fields. The choice of subbands lies on psychovisual experiments led in the laboratory. It was found that visual information is processed through 17 channels. In the second stage the use of the local band-limited contrast yields very interesting properties concerning the quantization. A scalar and vector quantization have been considered. In this latter case the vectors construction methodology preserves the main properties of the human visual system about perception of quantization impairments and takes into account the masking effect due to interaction between subbands with the same radial frequency but with different orientations. The vectors components are the local band limited contrasts Cij (m, n) defined as the ratio between the luminance Lij at the point (m, n), which belongs to the radial subband i and angular sector j and the average luminance at this location. Hence the vectors dimension depends on the orientation selectivity of the chosen decomposition. The low pass subband, which is nondirectional is scalar quantized. A methodology for automatic subsampling matrix design was also developed. The performance have been evaluated on a set of images in terms of peak SNR, true bit rates, and visual quality. For the latter, no impairments are visible at a distance of four times the height of the used high quality TV monitor. The SNRs are about 6 to 8 dB under the ones of classical subband image coding schemes when producing the same visual quality. Another particularity of this approach, due to the use of the local band limited contrast, lies in the structure of the reconstruction image error which is found to be highly correlated to the structure of the original image.

Visual Coding

This paper describes a new methodology in the design of psychovisual quantizers for a given visual subband image coding scheme. The quantizer design needs to specify a set of decision thresholds (Tk,k= 0, ...,l) and reconstruction levels (Lk,k= 0, ...,l? 1) for which any input level lying between the thresholdsTkandTk+1is mapped toLk, thekth reconstruction level. To specify the optimal quantization, psychophysical experiments based on the visibility of quantization noise have been conducted. The contrast masking function has been determined by considering complex signals, spatial frequency aspects of the quantized signals, and the spatial combination of degradations. The stimuli used have been characterized by their local band-limited contrast. With this representation, within a subband, the first important result is that the decision thresholds and the reconstruction levels of the quantization follow a linear law regardless of the importance of the contrast. From one subband to another the quantization step varies according to radial spatial frequency and orientation. In order to examine the masking effects between subbands, further experiments have been conducted and two other important results have been obtained. First, when it occurs, masking does not modify the linear behavior of the optimal quantization law. Secondly, masking is due to the adjacent angular subbands rather than the adjacent radial ones, even if a slight increase in the quantization step is observed in this last case.

Frequency and spatial pooling of visual differences for still image quality assessment

Objective image quality assessment techniques are currently based on the properties of the human visual system (HVS) essentially using early vision model. This type of approach allows to get the differences between original and distorted images in a perceptually space, so the outputs of the early vision model are perceptual distortion maps. In order to get one mark for the overall distortion, spatial pooling and frequency pooling in case of spatial frequency decomposition should be applied on these maps. In this paper, we present various methods to do this pooling. In order to represent the distorted image in a perceptual space, we use a multi-channel early vision model including an amplitude nonlinearity, a CSF, a subband decomposition and a masking function. For the pooling, Minkowski summation with various exponents is first tested as it is the most common pooling in literature. The second type of pooling proposed achieves a summation of all the degradations weighted by a function of the probability of their occurrence. Finally we propose a summation taking into account some higher perceptual factors in order to point out the region of interest used to weight the errors. The results are compared measuring the correlation between the distortion marks and the MOS.

Quality Assessment of Still Images

In this chapter, a description of evaluation methods to quantify the quality of impaired still images is proposed. The presentation starts with an overview of the mainly subjective methods recommended by both the International Telecommunication Union (ITU) and International Organization for Standardization (ISO) and widely used by Video Quality Experts Group (VQEG). Then, the algorithmic measures are investigated. In this context, low-complexity metrics such as Peak Signal to Noise Ratio (PSNR) and Mean Squared Error (MSE) are first presented to finally reach perceptual metrics. The general scheme of these latter is based on the Human Visual System (HVS) and exploits many properties such as the luminance adaptation, the spatial frequency sensitivity, the contrast and the masking effects. The performance evaluation of the objective quality metrics follows a methodology that is described.

Attentional mechanisms driven adaptive quantization and selective bit allocation scheme for H.264/AVC

Rate control algorithm adopted in H.264/AVC reference software shows several shortcomings that have been highlighted by different studies. For instance, in the baseline profile, the frame target bit-rate estimation assumes similar characteristics for all frames and the quantization parameter determination uses the Mean Absolute Difference for complexity estimation. Consequently, an inefficient bit allocation is performed leading to important quality variation of decoded sequences. A saliency-based rate-control is proposed in this paper to achieve bit-rate saving and improve perceived quality. The saliency map of each frame, simulating the human visual attention by a bottom-up approach, is used at the frame level to adjust the quantization parameter and at the macroblock level to guide the bit allocation process. Simulation results show that the proposed attentional model is well correlated to human behavior. When compared to JM15.0 reference software, at the frame level, the saliency map exploitation achieves bit-rate savings of up to 26%. At the MB level and under the same quality constraint, bit-rate improvement is up to 42% and buffer level variation is reduced by up to 71%.

New H.264 intra-rate estimation and inter-rate control driven by improved MAD-based Contrast Sensitivity

This paper aims to improve H.264 bit-rate control. The proposed algorithm is based on a new and efficient Rate-Quantization (R-Q) model for the intra frame. For the inter frame, we propose to replace the current use of MAD by a new MAD-based human Contrast Sensitivity (MAD-CS) which is a more accurate complexity measure. R-Q model for the intra frame results from extensive experiments. The optimal initial quantization parameter QP is based on both target bit-rate and complexity of I-frame. The I-frame target bit-rate is derived from the global target bit-rate by using a new non linear model. MAD-CS includes the contrast sensitivity of the human visual system and weights the absolute differences by the probability of their occurrence. Extensive simulation results show that the use of MAD-CS and the proposed R-Q model achieves better rate control for intra frames, reduces the bit-rates when compared to the H.264 rate control adopted in JM reference software, minimizes the peak to signal ratio variations among encoded pictures and increases significantly as well subjective visual quality (measured by psycho visual experiments) as objective one.

Masking effects in the quality assessment of coded images

Today, the use of objective quality metrics is well-known for the optimization of digital image processing systems. The work presented in this paper is about an algorithmic construction of an image quality criterion. This criterion takes into account the human visual system HVS properties in order to ensure the correspondence between objective measures given by the criterion and subjective notes given by a group of observers, and is decomposed functionally into three principal blocks. The first one corresponds to a perceptual image representation: a set of 17 frequential channels models the radial and angular selectivity of the HVS. The second block concerns the construction of the adaptation function of perception thresholds due to masking effects. Thanks to psychophysical experiments, the visibility thresholds of impairments are first measured in each individual channel, then in the presence of masking signals from other channels. The aim of this paper is to present these results and the masking model which takes into account both masking effects within channels and between channels. Finally in the third block, both frequential and spatial pooling are performed.

Comparison of No-Reference Image Quality Assessment Machine Learning-based Algorithms on Compressed Images

No-reference image quality metrics are of fundamental interest as they can be embedded in practical applications. The main goal of this paper is to perform a comparative study of seven well known no-reference learning-based image quality algorithms. To test the performance of these algorithms, three public databases are used. As a first step, the trial algorithms are compared when no new learning is performed. The second step investigates how the training set influences the results. The Spearman Rank Ordered Correlation Coefficient (SROCC) is utilized to measure and compare the performance. In addition, an hypothesis test is conducted to evaluate the statistical significance of performance of each tested algorithm.