An ROI multi-resolution compression method for 3D-HEVC

Chunli Ti; Yudong Guan; Guodong Xu; Yidan Teng; Xinyuan Miao

doi:10.1117/12.2274012

Abstract

3D High Efficiency Video Coding (3D-HEVC) provides a significant potential on increasing the compression ratio of multi-view RGB-D videos. However, the bit rate still rises dramatically with the improvement of the video resolution, which will bring challenges to the transmission network, especially the mobile network. This paper propose an ROI multi-resolution compression method for 3D-HEVC to better preserve the information in ROI on condition of limited bandwidth. This is realized primarily through ROI extraction and compression multi-resolution preprocessed video as alternative data according to the network conditions. At first, the semantic contours are detected by the modified structured forests to restrain the color textures inside objects. The ROI is then determined utilizing the contour neighborhood along with the face region and foreground area of the scene. Secondly, the RGB-D videos are divided into slices and compressed via 3D-HEVC under different resolutions for selection by the audiences and applications. Afterwards, the reconstructed low-resolution videos from 3D-HEVC encoder are directly up-sampled via Laplace transformation and used to replace the non-ROI areas of the high-resolution videos. Finally, the ROI multi-resolution compressed slices are obtained by compressing the ROI preprocessed videos with 3D-HEVC. The temporal and special details of non-ROI are reduced in the low-resolution videos, so the ROI will be better preserved by the encoder automatically. Experiments indicate that the proposed method can keep the key high-frequency information with subjective significance while the bit rate is reduced.

Conference Presentation

Citation Download Citation

Chunli Ti, Chunli Ti, Yudong Guan, Yudong Guan, Guodong Xu, Guodong Xu, Yidan Teng, Yidan Teng, Xinyuan Miao, Xinyuan Miao, } "An ROI multi-resolution compression method for 3D-HEVC", Proc. SPIE 10396, Applications of Digital Image Processing XL, 103960Y (19 September 2017); doi: 10.1117/12.2274012; https://doi.org/10.1117/12.2274012

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