Deep region segmentation-based intra prediction for depth video coding

Zhang, Jing; Hou, Yonghong; Zhang, Zhe; Jin, Dengchao; Zhang, Peihan; Li, Ge

doi:10.1007/s11042-022-13344-7

Cited by 3 publications

(1 citation statement)

References 42 publications

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“…Furthermore, a frame-level training strategy is devised for the information edge representation. Xie et al [28] proposed a CNNbased edge detection system based on the idea of FNN and deep supervised networks, which showed promising results in edge detection by initializing the network structure and parameters using a pre-trained constructed VGGNet and by amalgamating visual responses across multiple scales and levels.…”

Section: Fast Algorithm Based On Deep Learningmentioning

confidence: 99%

Efficient CU Decision Algorithm for VVC 3D Video Depth Map Using GLCM and Extra Trees

Wang,

Zhang

2023

Electronics

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The new generation of 3D video is an international frontier research hotspot. However, the large amount of data and high complexity are core problems to be solved urgently in 3D video coding. The latest generation of video coding standard versatile video coding (VVC) adopts the quad-tree with nested multi-type tree (QTMT) partition structure, and the coding efficiency is much higher than other coding standards. However, the current research work undertaken for VVC is less for 3D video. In light of this context, we propose a fast coding unit (CU) decision algorithm based on the gray level co-occurrence matrix (GLCM) and Extra trees for the characteristics of the depth map in 3D video. In the first stage, we introduce an edge detection algorithm using GLCM to classify the CU in the depth map into smooth and complex edge blocks based on the extracted features. Subsequently, the extracted features from the CUs, classified as complex edge blocks in the first stage, are fed into the constructed Extra trees model to make a fast decision on the partition type of that CU and avoid calculating unnecessary rate-distortion cost. Experimental results show that the overall algorithm can effectively reduce the coding time by 36.27–51.98%, while the Bjøntegaard delta bit rate (BDBR) is only increased by 0.24% on average which is negligible, all reflecting the superior performance of our method. Moreover, our algorithm can effectively ensure video quality while saving much encoding time compared with other algorithms.

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Section: Fast Algorithm Based On Deep Learningmentioning

confidence: 99%

Efficient CU Decision Algorithm for VVC 3D Video Depth Map Using GLCM and Extra Trees

Wang,

Zhang

2023

Electronics

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CNN-LNN Based Fast CU Partitioning Decision for VVC 3D Video Depth Map Intra Coding

Wang

Zhang

2023

IEEE Access

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Image Virtual Viewpoint Generation Method under Hole Pixel Information Update

et al. 2022

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A virtual viewpoint generation method is proposed to address the problem of low fidelity in the generation of virtual viewpoints for images with overlapping pixel points. Virtual viewpoint generation factors such as overlaps, holes, cracks, and artifacts are analyzed and preprocessed. When the background of the hole is a simple texture, pheromone information around the hole is used as the support, a pixel at the edge of the hole is detected, and the hole is predicted at the same time, so that the hole area is filled in blocks. When the hole background has a relatively complex texture, the depth information of the hole pixels is updated with the inverse 3D transformation method, and the updated area pheromone is projected onto the auxiliary plane and compared with the known plane pixel auxiliary parameters. The hole filling is performed according to the symmetry of the pixel position of the auxiliary reference viewpoint plane to obtain the virtual viewpoint after optimization. The proposed method was validated using image quality metrics and objective evaluation metrics such as PSNR. The experimental results show that the proposed method could generate virtual viewpoints with high fidelity, excellent quality, and a short image-processing time, which effectively enhanced the virtual viewpoint generation performance.

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Deep region segmentation-based intra prediction for depth video coding

Cited by 3 publications

References 42 publications

Efficient CU Decision Algorithm for VVC 3D Video Depth Map Using GLCM and Extra Trees

Efficient CU Decision Algorithm for VVC 3D Video Depth Map Using GLCM and Extra Trees

CNN-LNN Based Fast CU Partitioning Decision for VVC 3D Video Depth Map Intra Coding

Image Virtual Viewpoint Generation Method under Hole Pixel Information Update

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