The existing depth video coding algorithms are generally based on in-loop depth filters, whose performance are unstable and easily affected by the outliers. In this paper, we design a joint weighted sparse representat...The existing depth video coding algorithms are generally based on in-loop depth filters, whose performance are unstable and easily affected by the outliers. In this paper, we design a joint weighted sparse representation-based median filter as the in-loop filter in depth video codec. It constructs depth candidate set which contains relevant neighboring depth pixel based on depth and intensity similarity weighted sparse coding, then the median operation is performed on this set to select a neighboring depth pixel as the result of the filtering. The experimental results indicate that the depth bitrate is reduced by about 9% compared with anchor method. It is confirmed that the proposed method is more effective in reducing the required depth bitrates for a given synthesis quality level.展开更多
In order to solve the hole-filling mismatch problem in virtual view synthesis, a three-step repairing(TSR) algorithm was proposed. Firstly, the image with marked holes is decomposed by the non-subsampled shear wave tr...In order to solve the hole-filling mismatch problem in virtual view synthesis, a three-step repairing(TSR) algorithm was proposed. Firstly, the image with marked holes is decomposed by the non-subsampled shear wave transform(NSST), which will generate high-/low-frequency sub-images with different resolutions. Then the improved Criminisi algorithm was used to repair the texture information in the high-frequency sub-images, while the improved curvature driven diffusion(CDD) algorithm was used to repair the low-frequency sub-images with the image structure information. Finally, the repaired parts of high-frequency and low-frequency sub-images are synthesized to obtain the final image through inverse NSST. Experiments show that the peak signal-to-noise ratio(PSNR) of the TSR algorithm is improved by an average of 2-3 dB and 1-2 dB compared with the Criminisi algorithm and the nearest neighbor interpolation(NNI) algorithm, respectively.展开更多
基金Supported by the National Natural Science Foundation of China(61462048)
文摘The existing depth video coding algorithms are generally based on in-loop depth filters, whose performance are unstable and easily affected by the outliers. In this paper, we design a joint weighted sparse representation-based median filter as the in-loop filter in depth video codec. It constructs depth candidate set which contains relevant neighboring depth pixel based on depth and intensity similarity weighted sparse coding, then the median operation is performed on this set to select a neighboring depth pixel as the result of the filtering. The experimental results indicate that the depth bitrate is reduced by about 9% compared with anchor method. It is confirmed that the proposed method is more effective in reducing the required depth bitrates for a given synthesis quality level.
基金supported by the National Natural Science Foundation of China (61834005, 61772417, 61602377, 61634004,61802304)the Shaanxi Province Key R&D Plan (2021GY-029)。
文摘In order to solve the hole-filling mismatch problem in virtual view synthesis, a three-step repairing(TSR) algorithm was proposed. Firstly, the image with marked holes is decomposed by the non-subsampled shear wave transform(NSST), which will generate high-/low-frequency sub-images with different resolutions. Then the improved Criminisi algorithm was used to repair the texture information in the high-frequency sub-images, while the improved curvature driven diffusion(CDD) algorithm was used to repair the low-frequency sub-images with the image structure information. Finally, the repaired parts of high-frequency and low-frequency sub-images are synthesized to obtain the final image through inverse NSST. Experiments show that the peak signal-to-noise ratio(PSNR) of the TSR algorithm is improved by an average of 2-3 dB and 1-2 dB compared with the Criminisi algorithm and the nearest neighbor interpolation(NNI) algorithm, respectively.
