This paper presents a methodology for the restoration of the visual quality of still images affected by coding noise. This quality restoration is achieved only by considering the additive coding noise and is therefore limited to an adaptive postprocessing filtering. It is based on a model of the human visual system that considers the relationship between visual stimuli and their visibility. This phenomenon known as masking is used as a criterion for the locally adaptive filtering design. An image transformation that yields visual stimuli tuned to the frequency and orientation according to the perceptual model is proposed. It allows a local measure of the masking of each perceptual stimulus considering the contrast between signal and estimated noise. This measure is obtained by analytic filtering. Processing schemes are presented with applications to the discrete cosine transform (DCT) and subband coded images. One proposed solution considers the characteristics of DCT coding noise for the estimation of the noise. Another solution is based on a "blind" neural estimation of the noise characteristics. Experimental results of the proposed approaches show significant improvements of the visual quality, which validates our perceptual model and filtering.

This paper investigates the development of a new image quality criterion based on the psychovisual model of tuned channels and more particularly the phenomenon of masking. The masking model parameters have been evaluated by psychovisual tests assuming a logarithmic relationship between the visibility threshold and the contrast value of the background for a given perceptual channel. The masking has the consequence that only a part of the noise of a noisy image is really visible and affects the visual quality of the image. The idea of the proposed criterion is to evaluate the image quality after the cancellation of the masked noise defined as the invisible noise. The criterion has been used in order to compare different coders and different post-processings of noisy images.

The aim of image restoration is to find an estimate of the ideal image using a priori information about blur and/or noise and/or the ideal image. Classical criterion are minimum least square, minimum mean square error or maximum a posteriori probability. The choice of the criterion used to measure the estimation quality is crucial for the design of the restoration algorithm. The authors propose a new criterion based on a visual model: it is based on perceptual masking. Thereafter, they propose a new restoration algorithm dealing only with additive noise. The perceptual components of the image to restore which are corrupted by an additive noise above a visibility threshold are simply set to zero. Some results obtained for the post-processing of JPEG images are presented.>

In standard DCT coding schemes like MPEG, the sequences compression is achieved by motion compensation, transformation, quantization, and entropy coding. In this paper, we have followed the same path by adapting to the image signal the elements of the coding scheme. The motion compensation is achieved by a block-matching method, where the size of the blocks is adapted to the signal. Great attention has been paid to the relevance of the motion field. Combined with the motion compensation, the two fields of each frame are merged, taking into account the measured motion vectors, to compose a pseudo-progressive frame. The encoding is applied to this `motion-compensated progressive' frame. A wavelet decomposition is then applied on each (inter or intra) frame. Such a transform, intrinsically owning linear- phase and perfect reconstruction properties, has been optimized for maximizing a perceptually weighted coding gain. The wavelet coefficients are thereafter vector-quantized, in order to reach the maximum perceptual SNR : frequency weighting is taken into account. The relevance of the measured vector field allows a precise spatio-temporal quantization optimization. The vectors are entropy coded taking into account the remaining inter-band dependence, by an adapted entropy code. Results obtained from 1 Mbit/s to 8 Mbit/s are shown for moving sequences at the conference.

This paper presents a human visual quality criterion for still monochrome pictures. The main idea is based on the decomposition of the signal in tuned channels with respect to the location in the visual field, the orientation and the frequency. The specific sensitivity of each channel and the self-masking are taken into account to evaluate the quality of the picture.