计算机科学 ›› 2021, Vol. 48 ›› Issue (5): 155-162.doi: 10.11896/jsjkx.200800079

• 计算机图形学&多媒体 • 上一篇    下一篇

基于非局部低秩和自适应量化约束先验的HEVC后处理算法

徐艺菲, 熊淑华, 孙伟恒, 何小海, 陈洪刚   

  1. 四川大学电子信息学院 成都610065
  • 收稿日期:2020-08-13 修回日期:2020-09-17 出版日期:2021-05-15 发布日期:2021-05-09
  • 通讯作者: 何小海(hxh@scu.edu.cn)
  • 基金资助:
    国家自然科学基金(61871279);成都市产业集群协同创新项目(2016-XT00-00015-GX)

HEVC Post-processing Algorithm Based on Non-local Low-rank and Adaptive Quantization Constraint Prior

XU Yi-fei, XIONG Shu-hua, SUN Wei-heng, HE Xiao-hai, CHEN Hong-gang   

  1. School of Electronics and Information Engineering,Sichuan University,Chengdu 610065,China
  • Received:2020-08-13 Revised:2020-09-17 Online:2021-05-15 Published:2021-05-09
  • About author:XU Yi-fei,born in 1996,postgraduate.Her main research interests includeimage/video coding and so on.(xyf103553@163.com)
    HE Xiao-hai,born in 1964,Ph.D,professor,Ph.D supervisor.His main research interests include image processing,pattern recognition and image communication.
  • Supported by:
    National Natural Science Foundation of China (61871279) and Chengdu Industrial Cluster Collaborative Innovation Project(2016-XT00-00015-GX).

摘要: 经高效视频编解码标准HEVC压缩后的视频在高压缩比、低码率的情况下存在明显的压缩效应。针对该问题,提出了一种基于非局部低秩(Non-local Low-rank,NLLR)和自适应量化约束(Adaptive Quantization Constraint,AQC)先验的HEVC后处理算法。该算法首先构造在最大后验概率框架下的优化问题,然后利用解码后的压缩视频和量化参数QP获取非局部低秩和自适应量化约束先验信息,最后利用split-Bregman迭代算法来解决所提的优化问题,从而有效去除压缩效应,提升重建视频质量。其中,非局部低秩先验通过构建基于相似块聚类的非局部低秩模型来获得;自适应量化约束先验通过联合不同量化参数QP下的约束特性与视频的DCT域块活动性来获得。实验结果表明,在同等码率的情况下,与HEVC标准相比,所提算法在帧内编码模式下可以达到平均0.259 7 dB的PSNR提升,在帧间编码模式下可以达到平均0.282 8 dB的PSNR提升。

关键词: HEVC后处理, split-Bregman迭代算法, 非局部低秩先验, 自适应量化约束

Abstract: Video compressed by HEVC has an obvious compression effect under the condition of a high compression ratio and a low bit rate.To solve this problem,a post-processing algorithm of HEVC based on non-local low-rank (NLLR) and adaptive quantization constraint (AQC) prior is proposed.This algorithm firstly constructs the optimization problem within the maximum priori probability framework.Then,the decoded compressed video and quantization parameters QP are used to obtain the NLLR and AQC prior information.Finally,the split-Bregman iterative algorithm is used to solve the optimization problem,so as to effectively remove the compression effect and improve the quality of reconstructed video.Among them,the NLLR prior is obtained by constructing the non-local low-rank model based on similar-block clustering.The AQC prior is obtained by combining the constraint characteristics under different quantization parameters QP and the DCT domain block activity of video.Experimental results show that the proposed algorithm can achieve an average PSNR improvement of 0.259 7 dB in intra-frame coding mode and an average PSNR improvement of 0.282 8 dB in inter-frame coding mode compared with HEVC standard at the same bit rate.

Key words: Adaptive quantization constraint, HEVC post-processing, Non-local low-rank prior, Split-Bregman iteration algorithm

中图分类号: 

  • TN919.8
[1]WAN S,YANG F Z.New Generation of High Efficiency Video Coding H.265/HEVC:Principles,Standards and Implementation[M].Beijing:Publishing House of Electronics Industry,2014:IV.
[2]ZHANG X,XIONG R,LIN W,et al.Low-Rank based Nonlocal Adaptive Loop Filter for High-Efficiency Video Compression[J].IEEE Transactions on Circuits and Systems for Video Technology,2017,27(10):2177-2188.
[3]HE X Y,DUAN L Y,LIN W Y.Deep Residual Network Based HEVC Compressed Videos Enhancement[J].Computer Science,2019,46(3):88-91.
[4]GEMAN D,REYNOLDS G.Constrained Restoration and theRecovery of Discontinuities[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2002,14(3):367-383.
[5]MUMFORD D,SHAH J.Optimal Approximations by Piecewise Smooth Functions and Associated Variational Problems[J].Communications on Pure & Applied Mathematics,1989,42(5):577-685.
[6]ZHANG J,ZHAO D B,GAO W.Group-Based Sparse Representation for Image Restoration[J].IEEE Transactions on Image Processing,2014,23(8):3336-3351.
[7]LI A,CHEN D Y,SUN G L,et al.Sparse Representation-Based Image Restoration via Nonlocal Supervised Coding[J].Optical Review,2016,23(5):776-783.
[8]LIU H F,XIONG R Q,LIU D,et al.Image Denoising via Low Rank Regularization Exploiting Intra and Inter Patch Correlation[J].IEEE Transactions on Circuits and Systems for Video Technology,2018,28(12):3321-3332.
[9]PARK S H,KIM D S.Theory of Projection onto the Narrow Quantization Constraint Set and Its Application[J].IEEE Transactions on Image Processing,1999,8(10):1361-1373.
[10]YANG Y Y,GALATSANOS N P.Removal of Compression Artifacts Using Projections onto Convex Sets and Line Process Modeling[J].IEEE Transactions on Image Processing,1997,6(10):1345-1357.
[11]ZHAO C,ZHANG J,MA S W,et al.Reducing Image Compression Artifacts by Structural Sparse Representation and Quantization Constraint Prior[J].IEEE Transactions on Circuits and Systems for Video Technology,2017,27(10):2057-2071.
[12]ZHANG J,XIONG R Q,ZHAO C,et al.CON-COLOR:Con-strained Non-Convex Low-Rank Model for Image Deblocking[J].IEEE Transactions on Image Processing,2016,25(3):1246-1259.
[13]ZHANG J,ZHAO D B,XIONG R Q,et al.Image Restoration Using Joint Statistical Modeling in a Space-Transform Domain[J].IEEE Transactions on Circuits & Systems for Video Technology,2014,24(6):915-928.
[14]TIRER T,GIRYES R.Image Restoration by Iterative Denoising and Backward Projections[J].IEEE Transactions on Image Processing,2019,28(3):1220-1234.
[15]ZHANG X F,LIN W S,XIONG R Q,et al.Low-Rank Decomposition Based Restoration of Com-pressed Images via Adaptive Noise Estimation[J].IEEE Transactions on Image Processing,2016,25(9):4158-4171.
[16]JUNG C K,JIAO L C,QI H T,et al.Image De-blocking viaSparse Representation[J].Signal Processing Image Communication,2012,27(6):663-677.
[17]ZHANG X F,XIONG R Q,FAN X P,et al.Compression Artifact Reduction by Overlapped-Block Transform Coefficient Estimation with Block Similarity[J].IEEE Transactions on Image Processing,2013,22(12):4613-4626.
[18]BJONTEGARD G.Calculation of Average PS-NR Differencesbetween RD-curves[C]//13th Video Coding Experts Group Meeting.Austin:ITU-T VCEG-M33,2001:2-4.
[19]STANKIEWICZ O,WEGNER K,KARWOW-SKI D,et al.HEVC Encoding Assisted with Noise Reduction[J].International Journal of Electronics and Telecommunications,2018,64(3):285-292.
[1] 李发光, 伊力哈木·亚尔买买提.
基于改进CenterNet的航拍绝缘子缺陷实时检测模型
Real-time Detection Model of Insulator Defect Based on Improved CenterNet
计算机科学, 2022, 49(5): 84-91. https://doi.org/10.11896/jsjkx.210400142
[2] 何权奇, 余飞鸿.
面向无线网络相机的低功耗架构研究综述
Review of Low Power Architecture for Wireless Network Cameras
计算机科学, 2021, 48(6A): 369-373. https://doi.org/10.11896/jsjkx.201100099
[3] 刘东, 王叶斐, 林建平, 马海川, 杨闰宇.
端到端优化的图像压缩技术进展
Advances in End-to-End Optimized Image Compression Technologies
计算机科学, 2021, 48(3): 1-8. https://doi.org/10.11896/jsjkx.201100134
[4] 蔡于涵,熊淑华,孙伟恒,Karn Pradeep,何小海.
基于运动矢量细化的帧率上变换与HEVC结合的视频压缩算法
Video Compression Algorithm Combining Frame Rate Up-conversion with HEVC Standard Based on Motion Vector Refinement
计算机科学, 2020, 47(2): 76-82. https://doi.org/10.11896/jsjkx.190500092
[5] 张晶晶, 张爱华, 纪海峰.
基于小波与分形相结合的图像压缩编码
Image Compression Encoding Based on Wavelet Transform and Fractal
计算机科学, 2019, 46(8): 310-314. https://doi.org/10.11896/j.issn.1002-137X.2019.08.051
[6] 徐婧瑶, 王祖林, 徐迈.
基于深度学习的视频转码快速算法
Deep Learning Based Fast VideoTranscoding Algorithm
计算机科学, 2019, 46(3): 113-118. https://doi.org/10.11896/j.issn.1002-137X.2019.03.016
[7] 郭红伟, 骆洪军, 刘帅, 牛林, 杨波.
一种改进的R-λ模型码率控制算法
Improved R-λ Model Based Rate Control Algorithm
计算机科学, 2019, 46(3): 142-147. https://doi.org/10.11896/j.issn.1002-137X.2019.03.021
[8] 赵振兵, 崔雅萍, 戚银城, 杜丽群, 张珂, 翟永杰.
基于改进的R-FCN航拍巡线图像中的绝缘子检测方法
Detection Method of Insulator in Aerial Inspection Image Based on Modified R-FCN
计算机科学, 2019, 46(3): 159-163. https://doi.org/10.11896/j.issn.1002-137X.2019.03.024
[9] 衡阳, 陈峰, 徐剑峰, 汤敏.
基于压缩感知的心脏磁共振快速成像的应用现状与发展趋势
Application Status and Development Trends of Cardiac Magnetic Resonance Fast Imaging Based on Compressed Sensing Theory
计算机科学, 2019, 46(1): 36-44. https://doi.org/10.11896/j.issn.1002-137X.2019.01.006
[10] 杜秀丽, 胡兴, 陈波, 邱少明.
基于加权非局部相似性的视频压缩感知多假设重构算法
Multi-hypothesis Reconstruction Algorithm of DCVS Based on Weighted Non-local Similarity
计算机科学, 2019, 46(1): 291-296. https://doi.org/10.11896/j.issn.1002-137X.2019.01.045
[11] 刘菁华, 陈婧.
一种基于平面运动视差不变性的立体视频整帧丢失重建技术
Whole Frame Loss Concealment Method for Stereo Video Basedon Disparity Consistence
计算机科学, 2018, 45(6): 270-274. https://doi.org/10.11896/j.issn.1002-137X.2018.06.048
[12] 田伟, 刘浩, 陈根龙, 宫晓蕙.
面向分块压缩感知的交叉子集导引自适应观测
Cross Subset-guided Adaptive Measurement for Block Compressive Sensing
计算机科学, 2020, 47(12): 190-196. https://doi.org/10.11896/jsjkx.200800197
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!