Methods of the Definition Analysis of Fine Details of Images

Definition is one of the most important parameters of the color image quality and is determined by the resolution of channel brightness and chromaticity. System resolution is traditionally determined by a number of the television lines, calculated on the maximal spatial frequency value at which threshold contrast of the reproduced image is provided. Traditional methods of definition analysis are developed for standard analog color TV systems. Specific kind of distortions in digital vision systems is associated with the restrictions imposed by a particular compression algorithm, used for handling static and dynamic images. Such distortions may lead to an inconsistency between a subjective estimate of the decoded image quality and the program estimate based on the standard calculation methods. Till now, the most reliable way of image quality estimation is the method of subjective estimation which allows estimating serviceability of a vision system on the basis of visual perception of the decoded image. Procedures of subjective estimation demand great amount of tests and a lot of time. In practice, this method is quite laborious and restricts making control, tuning and optimization of the codec parameters. The most frequently used root-mean-square criterion (RMS) for the analysis of static image quality does not always correspond to the subjective estimation of fine details definition, since a human vision system processes an image on local characteristic features, rather than averaging it elementwise. In particular, RMS criterion can give "good" quality estimations in vision systems even at disappearance of fine details in low contrast image after a digital compression. A number of leading firms suggest hardware and software for the objective analysis of dynamic image quality of MPEG standard (Glasman, 2004). For example Tektronix PQA 300 analyzer; Snell & Wilcox Mosalina software; Pixelmetrix DVStation device. Principles of image quality estimation in these devices are various. For example, PQA 300 analyzer measures image quality on algorithm of “Just Noticeable Difference – JND”, developed by Sarnoff Corporation. PQA 300 analyzer carries out a series of measurements for each test sequence of images and forms common PQR estimation on the basis of JND measurements which is close to subjective estimations. To make objective analysis of image quality Snell & Wilcox firm offers a PAR method – Picture Appraisal Rating. PAR technology systems control artifacts created by compression