Depth video coding method on basis of foveal JND (just noticeable distortion) model

Abstract

The invention discloses a depth video coding method on the basis of a foveal JND (just noticeable distortion) model. The depth video coding method comprises the following steps of: firstly, calculating the global JND of each pixel in each frame in a middle virtual viewpoint video obtained by carrying out middle virtual viewpoint rendering on color videos at left and right viewpoints and depth videos at left and right viewpoints; then, according to the global JND, obtaining a middle virtual viewpoint video subjected to geometrical offset; according to the geometrical offset, obtaining the maximum tolerable distortion video of the depth video at the left viewpoint; and finally, obtaining increasable coded quantization parameters of each macro block in each frame of the depth video at the left viewpoint according to the maximum tolerable distortion video. According to the method disclosed by the utility model, on the premise of maintaining the visual perception quality of virtual viewpoints, the encoding rate of the depth videos is effectively saved, the compressed coding efficiency of a depth video sequence is also greatly improved, and code streams can be saved by 11.00% to 23.34%.

Description

technical field [0001] The invention relates to a video signal processing method, in particular to a depth video encoding method based on a central fovea just perceptible distortion model. Background technique [0002] Currently, 3DTV and FTV widely use Multi-view Video plus Depth (MVD) for scene description. MVD includes the color and corresponding depth information of multiple viewpoints, and draws virtual viewpoints on the display after encoding, transmission, and decoding. In MVD, although the coding of multi-viewpoint color video has been extensively studied, among which the more applicable coding platforms are JMVM (Joint Multiview Video Model) and JMVC (Joint Multiview Video Coding), but the research on depth video coding is less. Depth video represents the relative distance from the camera to objects in 3D space, and is widely used in the fields of computer vision and computer imaging to describe 3D information. In addition, it is worth noting that the depth video ...

AI Technical Summary

Problems solved by technology

The FJND model makes full use of the perceptual redundancy in video and improves the efficiency of video compression. However, the JND model and FJND model established above are aimed at flat images or single-channel videos, and most of the resolutions are limited to CIF format. , does not consider the influence of depth video distortion on the quality and resolution of the drawn video, and cannot be well applied to MVD compression

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