Thierry Eude

On the distribution of the DCT coefficients

The paper presents a statistical study in order to improve the image compression rate in discrete cosine transform (DCT) based methods and especially for compression methods which are defined in JPEG norm. It is realised extensively, using a battery of parametric and non-parametric fit-tests and laws such as those of Cauchy, Laplace, Gauss, and a mixture distribution of Laplace and Gaussian. Considering the absence of results for these laws in the literature, the authors constructed adapted statistical tools for DCT coefficients analysis. Then, they show that these coefficients can be modelized by a Gaussian finite mixture distribution. They also comment on the obtained results for medical images applications and explain how the compression rate can be improve with them.<<ETX>>

An evaluation of quality metrics for compressed images based on human visual sensitivity

It is generally believed that the performance of current image coding techniques is not close enough to the fundamental limit on the bit rate at which high reception quality is maintained. For supporting the future applications that demand high image quality at very low bit rates, more efficient coding algorithms are expected. Among numerous approaches to reach this optimality, perceptual coding that matches the compression algorithm to the human perception mechanism has been considered as the most promising solution, and has recently become an important area of research. In addition to the investigation on perceptual coding, the need for a distortion metric for predicting the distortion has long been recognized in the image processing community. For still image evaluation, several distortion measures tied to some psycho-visual properties have been proposed. Examples of such measures are to be found in the literature. One significant problem associated with these metrics is that there is little information on how these measures perform in comparison to one another. The purpose of this paper is to provide a rigorous evaluation of four metrics which are designed for the evaluation of coded achromatic still images.

A new visual masking tool for JPEG2000

Summary form only given. A nonuniform quantization scheme, based on the perceptual relevance of each channel signal component, has been developed to exploit HVS properties. This strategy is based on the exploitation of the visual masking effect by performing a subband decomposition. An evaluation of the quality was performed using psychophysical measures for validation. The obtained results denote an improvement of the image quality when images are compressed combining intra channel masking visual effect. Furthermore, a compression gain of 10% can be reached depending on the image content.

Quality metrics for low-bit-rate coding

This paper considers the use of image quality metric for still image compression systems comparison. The peak signal to noise ratio (PSNR) is a commonly used quality metric in image compression. However it is also generally acknowledges that the PSNR is not a good measure for the prediction of perception of the quality of many different types of image from statistical evaluation by panels of observers. Consequently, for still image evaluation, many authors have proposed several distortion measures with the introduction of some human visual characteristics. But one significant problem associated with these metrics is that there is little information on how these measures perform in comparison to each other. The purpose of this paper is to provide a rigorous evaluation of two metrics to assess the quality of compressed images. The compression system used in this evaluation is the classical JPEG coder. Both objective and subjective tests were performed on a 250 natural images database with a panel of experimented and nonexperimented observers. The results are highly correlated with the complexity of the images under study. Nevertheless, a statistical evaluation of these metrics allows us to operate an objective classification.

Biometric Authentication by Keystroke Dynamics for Remote Evaluation with One-Class Classification

One-Class SVM is an unsupervised algorithm that learns a decision function from only one class for novelty detection: classifying new data as similar inlier or different outlier to the training set. In this article, we have applied the One-Class SVM to Keystroke Dynamics pattern recognition for user authentication in a remote evaluation system at Laval University. Since all of their students have a short and unique identifier at Laval University, this particular static text is used as the Keystroke Dynamics input for a user to build our own dataset. Then, we were able to identify weaknesses of such a system by evaluating the recognition accuracy depending on the number of signatures and as a function of their number of characters. Finally, we were able to show some correlations between the dispersion and mode of distributions of features characterizing the signatures and the recognition rate.

Visual masking model for perceptual color image compression

The quality of compressed images can be remarkabley improved if the dictates of the Human Visual System (HVS) requirements are followed. To achieve this goal, our strategy is to exploit human visual masking effect using a subband decomposition. A combination of both intra channel and inter channel masking property is then performed. This scheme exploits the lowpass character of the perceptual masking for natural color images in estimating the amount of available masking. Results obtained show an improvement of the quality of reconstructed color images compared to existing compression schemes like standard JPEG2000.