Pixelwise adaptive prediction-based lossless reference frame compression algorithm for video coding

Weizhe Xu; Fangfa Fu; Yao Wang; Jinxiang Wang

doi:10.1117/1.JEI.27.3.033022

25 May 2018 Pixelwise adaptive prediction-based lossless reference frame compression algorithm for video coding

Weizhe Xu, Fangfa Fu, Yao Wang, Jinxiang Wang

Author Affiliations +

Journal of Electronic Imaging, Vol. 27, Issue 3, 033022 (May 2018). https://doi.org/10.1117/1.JEI.27.3.033022

Abstract

With the increasing size of video sequences, the dramatically increased access bandwidth and power consumption for fetching and storing reference frames with external memory during motion estimation become the bottleneck of realizing a practical video codec system. To reduce the access bandwidth and power consumption with no quality loss for reference frames, a lossless reference frame compression method based on pixelwise adaptive prediction is proposed. The algorithm allocates pixels from the first line and first column with horizontal prediction and vertical prediction separately. Meanwhile, two direction coefficients of horizontal and vertical are calculated to evaluate the similarity between two adjacent samples. For pixels in the rest locations, prediction value is assigned to every pixel adaptively from left, upper, and upper left adjacent reconstructed pixels according to the appropriate mode decision scheme based on similarity coefficients. At last, residuals are compressed by Huffman encoding process to generate bit-stream stored into external memory. By implementing the algorithm with high-efficiency video coding (HEVC) reference software HEVC test model-16.5 (HM-16.5), the experimental results show that the method can obtain 63.83% data reduction ratio averagely for luminance component.

Citation Download Citation

Weizhe Xu, Fangfa Fu, Yao Wang, and Jinxiang Wang "Pixelwise adaptive prediction-based lossless reference frame compression algorithm for video coding," Journal of Electronic Imaging 27(3), 033022 (25 May 2018). https://doi.org/10.1117/1.JEI.27.3.033022

Received: 12 December 2017; Accepted: 4 May 2018; Published: 25 May 2018

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