基于FSCD-CNN的深度图像快速帧内预测模式选择算法

doi:10.3969/j.issn.0255-8297.2021.03.009

Abstract

Abstract: In view of the coding complexity caused by the encoding format of multiview video plus depth map and the quadtree coding structure in 3D-HEVC, a fast intra prediction mode selection algorithm for depth images based on FSCD-CNN (fast selecting cu’s depth-convolutional neural network) is proposed. First, a training set is obtained by dividing the depth of the optimal depth map LCU (largest coding unit) of a depth video sequence as labels. Second, a FSCD-CNN network is constructed, which is suitable for deep decision-making of LCU. At last, the optimal division of LCU is achieved by carrying out the depth-division prediction of depth map LCU and skipping some coding mode decisions. Experimental results show that the proposed algorithm could reduce the coding time by 15% on average while maintaining the same coding performance as other relevant literatures, and verify the effectiveness and reliability of this method.

Key words: 3D-HEVC, depth map, largest coding unit (LCU), convolutional neural network (CNN), coding complexity

CLC Number:

TP319.4

CUI Pengtao, ZHANG Qian, LIU Jinghuai, ZHOU Chao, WANG Bin, SI Wen. FSCD-CNN Based Fast Mode Decision Algorithm for Intra-prediction in Depth Map Coding[J]. Journal of Applied Sciences, 2021, 39(3): 433-432.

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FSCD-CNN Based Fast Mode Decision Algorithm for Intra-prediction in Depth Map Coding

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