An IBP-CNN Based Fast Block Partition For Intra Prediction

Ren, Wenpeng; Su, Jia; Sun, Chuen-Tsai; Shi, Zhiping

doi:10.1109/pcs48520.2019.8954522

Cited by 6 publications

(1 citation statement)

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“…Some scholars corresponded the CNN outputs to CU partition characterization according to a quad-tree structure and achieved end-to-end CU prediction. Ren et al [36] proposed an intra-frame block partition CNN network, which adjusts the network output to a 4 × 4 matrix according to the quad-tree structure. Additionally, the matrix elements of the 4 × 4 matrix correspond to the depth values of the corresponding 16 × 16 CU blocks.…”

Section: Introductionmentioning

confidence: 99%

A Method to Reduce the Intra-Frame Prediction Complexity of HEVC Based on D-CNN

Wang

et al. 2023

Electronics

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Among a series of video coding standards jointly developed by ITU-T, VCEG, and MPEG, high-efficiency video coding (HEVC) is one of the most widely used video coding standards today. Therefore, it is still necessary to further reduce the coding complexity of HEVC. In the HEVC standard, a flexible partitioning procedure entitled “quad-tree partition” is proposed to significantly improve the coding efficiency, which, however, leads to high coding complexity. To reduce the coding complexity of the intra-frame prediction, this paper proposes a scheme based on a densely connected convolution neural network (D-CNN) to predict the partition of coding units (CUs). Firstly, a densely connected block was designed to improve the efficiency of the CU partition by fully extracting the pixel features of CTU. Then, efficient channel attention (ECA) and adaptive convolution kernel size were applied to a fast CU partition for the first time to capture the information of the D-CNN convolution channels. Finally, a threshold optimization strategy was formulated to select the best threshold for each depth to further balance the computation complexity of video coding and the performance of RD. The experimental results show that the proposed method reduces the encoding time of HEVC by 60.14%, with a negligible reduction in RD performance, which is better than the existing fast partitioning methods.

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Section: Introductionmentioning

confidence: 99%