Regression-based analysis of visual quality for denoised images
Andrii Rubel, Владимир Васильевич Лукин · 2017
Visual quality plays a key role in different image processing applications, in particular, in image denoising. Assessment of visual quality of denoised images allows assessing the effectiveness of filtering. Among image visual quality assessment approaches, the most reliable are experiments with observers to get mean opinion score (MOS). This paper aims to conduct prediction of MOS for denoised images based on several quantitative criteria. Probability of voting for expedience of denoising is used as MOS. Analysis is carried out for two filters - standard DCT filter and BM3D. Problems and challenges of such prediction are shown.