Razvan Iordache

Pyramidal perceptual filtering using Moon and Spencer contrast

A perceptual multiresolution filtering method (Moon and Spencer, 1945) based on human perception models is proposed. The main idea is to detect and remove the irrelevant structures at different scales using the just-noticeable contrast notion and luminance adaptation. The processing is done in the Laplacian and Gaussian pyramid decompositions of the image. The consistency of the method is demonstrated on a gray-level image.

Fixed-slope near-lossless context-based image compression

This paper generalizes trellis based near-lossless image compression schemes, in order to satisfy two constraints: a local one, which enforces an upper bound /spl epsiv/ for the reconstruction error at every pixel, and a global one, which penalizes the quantity R+/spl lambda/D (the rate and distortion over each line of the image). By changing /spl lambda/ we obtain gradual changes from lossless coding (/spl lambda//spl infin/) to DPCM near-lossless coding (/spl lambda/=0), resulting in a distinct rate-distortion curve for each tolerance /spl epsiv/. We propose here a trellis-based fixed-slope near-lossless compression approach, that fits the adaptive context coding scheme ASBOSC introduced in 1997. The experimental results over a set of test images in near-lossless mode are better than the results obtained with other near-lossless techniques.

A Wigner-Ville distribution-based image dissimilarity measure

The aim of this article is to present a new measure of image degradation based on a joint spatial/spatial-frequency representation using the Wigner-Ville distribution. The developed measure is tested on some known image degradations. The obtained results demonstrate the efficiency of the measure in predicting image quality as compared to classical distortion measures such as signal-to-noise ratio.

Block truncation coding using edginess information

An improvement of the well-known block truncation coding (BTC) method, introduced by Delp and Mitchell (1979), is proposed. The basic idea, derived from previous work of Beghdadi and Le Negrate (1989, 1991) on contrast enhancement and image filtering using a local contrast measure is to exploit edginess information in estimating both the quantization threshold and the reconstruction levels. The results obtained on actual images clearly show the efficiency of the method in preserving edge information compared to similar approaches.

Median-based post-processing for VQ image transmission over noisy channels

For image transmission over noisy channels, the VQ paradigm is a strong competitor. We propose a post-processing scheme that is able to improve the perceptual quality of the VQ reconstructed image, for various types of noisy channels, when it is combined with an appropriate index assignment. For binary symmetric channels, we use the index assignment resulting from the Linde, Buzo and Gray (1980) LBG design of the codebook, which ensures a low channel distortion. For the more challenging case of memory channels we consider index assignments specifically designed. We show that a significant perceptual improvement can be achieved with our post-processing scheme both for memoryless and memory channels.

Vector quantization with edge reconstruction

This paper proposes a vector quantization (VQ) scheme that improves the quality of the reconstructed image by correcting quantization artifacts. The basic idea of the coding scheme is to treat separately the blocks containing edges (edge blocks), as they contain important perceptual information. The edge blocks are identified using an edge detection algorithm and are labeled by an escape index, and their reconstruction is done via an interpolation procedure that exploits the spatial correlation in the image. The remaining blocks (smooth blocks) are coded using standard VQ.