High dynamic color image quality evaluation method based on feature similarity

Abstract

The invention discloses a high dynamic color image quality evaluation method based on feature similarity, The method comprises the following steps: 1, converting the original brightness into perceptual brightness, 2, calculating PC_R, PC_G and PC_B R of R of G and B color components of the color LDR image identical in phase, 3, calculating the gradient magnitude (GM) of R, G and B components of color LDR images GM_R, GM_G and GM_B; 4, integrating the PC feature map and the GM feature map into a quaternion matrix, and converting the feature map of color LDR images into pure imaginary form, 5, calculating feature similarity and realize quality evaluation. The invention retains the correlation of R, G and B color channels, and has high consistency with the subjective quality evaluation of human eyes. The algorithm has high evaluation accuracy for different distortions and low computational complexity, so it can achieve real-time quality evaluation.

Description

technical field [0001] The invention relates to the fields of color image quality evaluation and high dynamic image processing, in particular to a high dynamic color image quality evaluation method. Background technique [0002] The brightness perceived by the human eye from the real world can reach 10 5 cd / m 2 . Currently, most cameras and display devices only support Low Dynamic Range (LDR) images with a contrast ratio of about 100:1 to 1000:1, which leads to problems such as "degraded viewing quality and unrealistic scenes". High Dynamic Range (HDR) images pursue more complete information that the human eye can see, and can record all the brightness information of the visible area in the scene even under extreme lighting conditions. However, HDR images experience various types of degradation during acquisition, processing, compression, and storage, seriously affecting the viewer's visual experience. Subjective evaluation methods cannot be applied in practice due to fa...

AI Technical Summary

Problems solved by technology

[0004] However, the HDR-VDP-2, HDR-VDP-2.2 and HDR-VQM algorithms mentioned above have high complexity and are not suitable for applications with strong real-time requirements; the simple combination of PU coding and traditional LDR algorithms is only for The brightness image only considers the characteristics of a wide brightness range, and does not take into account the color information that has a greater impact on HDR, which has certain limitations

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