|Search by item||HOME > Access full text > Search by item|
JBE, vol. 23, no. 6, pp.768-779, November, 2018
Image Processing of Pseudo-rate-distortion Function Based on MSSSIM and KL-Divergence, Using Multiple Video Processing Filters for Video Compression
Jinwuk Seok, Seunghyun Cho, Hui Yong Kim, and Jin Soo Choi
C.A E-mail: email@example.com
In this paper, we propose a novel video quality function for video processing based on MSSSIM to select an appropriate video processing filter and to accommodate multiple processing filters to each pixel block in a picture frame by a mathematical selection law so as to maintain video quality and to reduce the bitrate of compressed video. In viewpoint of video compression, since the properties of video quality and bitrate is different for each picture of video frames and for each areas in the same frame, it is difficult for the video filter with single property to satisfy the object of increasing video quality and decreasing bitrate. Consequently, to maintain the subjective video quality in spite of decreasing bitrate, we propose the methodology about the MSSSIM as the measure of subjective video quality, the KL-Divergence as the measure of bitrate, and the combination method of those two measurements. Moreover, using the proposed combinatorial measurement, when we use the multiple image filters with mutually different properties as a pre-processing filter for video, we can verify that it is possible to compress video with maintaining the video quality under decreasing the bitrate, as possible.
Keyword: MSSSIM, KL-Divergence, Video Filtering, pseudo-rate-distortion
 T. Dumas, A. Roumy, C. Guillemot "Image compression with stochastic winner-take-all auto-encoder". Proceeding of International Conference Acoustic, Speech and Signal Processing, New Orleans, USA, pp. 1512-1516
 K. Gregor, Y. LeCun "Learning representations by maximizing compression". arXiv:1108.1169, Aug. 2011
 C. Chou, 1995 , A Perceptually Tuned Subband Image Coder Based on the Measure of Just-Noticeable-Distortion Profile , IEEE Transactions on Circuits and Systems for Video Tech, vol. 5, Issue 6, pp.467-476, Dec. 1995.
 Wang, Z. Simoncelli, E.P. Bovik, A.C. "Multiscale structural similarity for image quality assessment". Conference Record of the Thirty- Seventh Asilomar Conference on Signals, Systems and Computers, Vol. 2, pp. 1398–1402, Feb. 2004. doi:10.1109/ACSSC.2003. 1292216.
 F. Bossen, “Common test conditions and software reference configurations,” The 8th JCT-VC meeting, JCT-VC H1100, San Jose, CA, Jan. 2012.
 Y. Dai, D. Liu, and F. Wu, “A Convolutional Neural Network Approach for Post-Processing in HEVC Intra Coding,” Proceeding of the 23rd International Conference on Multimedia Modeling, Reykjavik, Iceland, pp.28-39, Jan. 2017.16
 T. Wang, M. Chen, and H. Chao, “A Novel Deep Learning-Based Method of Improving Coding Efficiency from the Decoder-end for HEVC,” Proceeding of Data Compression Conference, Snowbird, USA pp.410-419, April 2017.
 Brian F. Doolin, Clyde F. Martin, Introduction to Differential Geometry for Engineers, New york, pp. 115-139, 1990.