Cross-validation depth map quality evaluation method combined with jnd model

Abstract

The invention discloses a cross validation depth map quality evaluation method combined with a JND model. According to the method, a color map corresponding to a depth map and a color map on an auxiliary viewpoint are utilized to obtain a difference image; the number of pixels at each coordinate in the color map on the auxiliary viewpoint to which the depth map and the color map corresponding to the depth map are mapped is utilized to obtain a shielding mask; then the shielding mask is utilized to remove pixel points shielded in the difference image to obtain a difference image after deshielding; then the color map on the auxiliary viewpoint is divided into a flat region, an edge region and a texture region to obtain a region marking map; a JND model is introduced, combined with the region marking map, an error visual threshold of each pixel point in the color map on the auxiliary viewpoint is obtained; and finally, according to the deshielded difference image and the error visual threshold, a depth error map is obtained, and the rate of error pixel points in the depth map is further obtained as a quality evaluation value. The advantage is that the method can effectively improve consistency of an evaluation result and the quality of virtual viewpoints obtained by drawing.

Description

technical field [0001] The invention relates to an image quality evaluation method, in particular to a cross-validation depth map quality evaluation method combined with a JND (Just-noticeable-distortion, just noticeable distortion) model. Background technique [0002] In recent years, video technology has developed rapidly, and many new applications have emerged, such as 3D video and Free Viewpoint Video (FVV, Free Viewpoint Video). Compared with traditional two-dimensional video, 3D video provides depth information, resulting in a more realistic visual experience. Depth maps play a fundamental role in many 3D video applications, for example, depth maps can be used to generate arbitrary new viewpoint images by interpolating or extrapolating images at available viewpoints; moreover, a high-quality depth map is an important tool for solving problems in computer vision. provided assistance with challenging problems. The performance of many 3D video applications benefits from...

AI Technical Summary

Problems solved by technology

The no-reference depth map quality assessment scheme proposed by Xiang et al. detects errors by matching the edges of the color image and the depth map, and calculates the dead pixel rate to evaluate the quality of the depth map, which has a good consistency with the quality of the drawn virtual image. , but this scheme only considers the errors near the edge, ignoring other smooth areas, only some error pixels are detected, and the different attributes and error distribution of the scene have a great impact on the performance of the scheme

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