Details

Autor(en) / Beteiligte

• Lukin, Vladimir
• Bataeva, Ekaterina
• Abramov, Sergey

Titel

Saliency map in image visual quality assessment and processing

Ist Teil von

• Radìoelektronnì ì komp'ûternì sistemi (Online), 2023-03 (1), p.112-121

Ort / Verlag

National Aerospace University «Kharkiv Aviation Institute

Erscheinungsjahr

2023

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• Images are mainly viewed and analyzed by humans. Because of this, in the characterization of image quality and effectiveness of image processing, it is necessary to take into account the peculiarities of the human vision system and cognition that are very complex. Saliency maps as well as priority and meaning maps introduced recently are the attempts to incorporate specific features of human vision into image analysis and processing fields. Many authors that consider the aforementioned maps consider them from different viewpoints. Thus, the basic subject of this paper is the factors that influence and determine these maps. Among such factors, there are low-level features as well as social and psychological ones such as emotions, age, and life values. The main goal of this paper is to give a brief survey of these factors and to consider how maps are already used in image quality assessment and processing as well as how they can be employed in the future. The tasks of the paper are to provide a definition of saliency, priority, and meaning maps, to analyze the factors that influence these maps, and to evaluate what improvement can be obtained due to taking maps into account in the assessment of image visual quality and such image processing operations as quality assessment, denoising, and lossy compression. The main result is that, by taking saliency maps into account, image quality assessment and processing efficiency can be sufficiently improved, especially for applications oriented on image viewing and analysis by observers or customers. This can be done by the simple weighting of local estimates of a given metric with further aggregation as well as by approaches based on neural networks. Using different quantitative criteria, we show what positive results can be got due to incorporating maps into quality assessment and image processing. As conclusion, we present possible directions of future research that are mainly related to an adaptation of denoising and lossy compression parameters to peculiarities of human attention.