Abstract
In the recent years, many objective image quality assessment methods have been proposed by different researchers, leading to a significant increase in their correlation with subjective quality evaluations. Although many recently proposed image quality assessment methods, particularly full-reference metrics, are in some cases highly correlated with the perception of individual distortions, there is still a need for their verification and adjustment for the case when images are affected by multiple distortions. Since one of the possible approaches is the application of combined metrics, their analysis and optimization are discussed in this paper. Two approaches to metrics’ combination have been analyzed that are based on the weighted product and the proposed weighted sum with additional exponential weights. The validation of the proposed approach, carried out using four currently available image datasets, containing multiply distorted images together with the gathered subjective quality scores, indicates a meaningful increase of correlations of the optimized combined metrics with subjective opinions for all datasets.
Highlights
The increasing popularity and availability of relatively cheap cameras, as well as electronic mobile devices, equipped with visual sensors, undoubtedly causes a dynamic growth of applicability of image and video analysis in many tasks
Using the weights a in Equation (6), different ranges of metrics’ variation are taken into account. Using both a and w coefficients, the combined metric can be optimized, i.e., its better values of Pearson Linear Correlation Coefficients (PCC) and/or Spearman Rank Order Correlation Coefficients (SROCC) can be provided in comparison to elementary metrics used as inputs for the combined metric
An initial verification of the usefulness of the proposed approach for the FR quality assessment of multiply distorted images has been made primarily for the metrics listed in Table 1 using the four considered datasets independently
Summary
The increasing popularity and availability of relatively cheap cameras, as well as electronic mobile devices, equipped with visual sensors, undoubtedly causes a dynamic growth of applicability of image and video analysis in many tasks. Some other applications are related to non-destructive testing, data fusion from various sensors, and many others, related to modern Industry 4.0 solutions Another factor, influencing the growing popularity of image analysis, is the development of some freeware libraries, such as OpenCV, that makes it possible to perform many tasks in real-time, especially with hardware support provided by modern Graphics Processing Units (GPU). Machine and computer vision algorithms typically utilize natural images, which may be subject to various distortions, occurring during their acquisition and caused by, e.g., lossy compression or the presence of transmission errors This situation is typical for modern electronic devices, such as cameras, phones, and some other gadgets where image data are subject to several nonlinear transformations before recording. The ability to detect such distortions and assess the overall image quality is an important challenge given the reliability of the results obtained from their analysis
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