Авіаційно-космічна техніка та технологія (Nov 2021)
Using visual metrics to analyze lossy compression of noisy images
Abstract
The subject of the article is to analyze the effectiveness of lossy image compression using a BPG encoder using visual metrics as a quality criterion. The aim is to confirm the existence of an operating point for images of varying complexity for visual quality metrics. The objectives of the paper are the following: to analyze for a set of images of varying complexity, where images are distorted by additive white Gaussian noise with different variance values, build and analyze dependencies for visual image quality metrics, provide recommendations on the choice of parameters for compression in the vicinity of the operating point. The methods used are the following: methods of mathematical statistics; methods of digital image processing. The following results were obtained. Dependencies of visual quality metrics for images of various degrees of complexity affected by noise with variance equal to 64, 100, and 196. It can be seen from the constructed dependence that a working point is present for images of medium and low complexity for both the PSNR-HVS-M and MS-SSIM metrics. Recommendations are given for choosing a parameter for compression based on the obtained dependencies. Conclusions. Scientific novelty of the obtained results is the following: for a new compression method using Better Portable Graphics (BPG), research has been conducted and the existence of an operating point for visual quality metrics has been proven, previously such studies were conducted only for the PSNR metric.The test images were distorted by additive white Gaussian noise and then compressed using the methods implemented in the BPG encoder. The images were compressed with different values of the Q parameter, which made it possible to estimate the image compression quality at different values of compression ratio. The resulting data made it possible to visualize the dependence of the visual image quality metric on the Q parameter. Based on the obtained dependencies, it can be concluded that the operating point is present both for the PSNR-HVS-M metric and for the MS-SSIM for images of medium and low complexity, it is also worth noting that, especially clearly, the operating point is noticeable at large noise variance values. As a recommendation, a formula is presented for calculating the value of the compression control parameter (for the case with the BPG encoder, it is the Q parameter) for images distorted by noise with variance varying within a wide range, on the assumption that the noise variance is a priori known or estimated with high accuracy.
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