A new objective, full-reference metrics of image quality is proposed in this paper. It should match perceptual (subjective) image quality assessment in a better way. The proposed method consists of two quality measures which separately indicate image quality on edges and in texture areas which are calculated in a three-step algorithm. The ?soft mask? is initially found for separation in edge and texture areas. Then, two MSEs (mean square error) with corresponding two PSNRs (peak signal-to-noise ratio) for edge and texture are calculated using soft mask as the weighting factor. Finally, the obtained two PSNRs are re-calculated into the two quality indices for edges and texture. Additionally, the separation factor, defined as percentage of edge areas in image, is considered, describing the influence of the image content on perceptual assessment. The proposed 2D metrics is especially suited for evaluations of different interpolation and compression algorithms.
Objective evaluation of a subjective image quality assessment plays a significant role in the various image processing applications, such as compression, interpolation and noise reduction. The subjective image quality assessment does not only depend on some objective measurable artefacts, but also on image content and kind of distortions. Thus, a multi‐parameter prediction of the objective image quality assessment is proposed in this study. The prediction parameters are found minimising the mean square error related to the known subjective image quality measure (DMOS). This approach includes mostly used image quality metrics (peak signal‐to‐noise ratio, multi‐scale structural similarity image measure, feature similarity image measure, video quality measure) and two‐dimensional image quality metrics (2D IQM). The proposed multi‐parameter prediction has been verified on the test image database (LIVE) for compression, noise and blur distortions with available subjective image quality measures (DMOS). More reliable estimations are obtained using multi‐parameter prediction instead of only one measure. The best results are reached when an image content indicator is combined with the 2D IQM measure separately for different kinds of distortions.
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