The training of Karhunen---Loève transform matrix and its application for H.264 intra coding

Authors:
Yi Gao;Jiazhong Chen;Shengsheng Yu;Jingli Zhou;Lai-Man Po
Affiliations:
Department of Computer Science, Huazhong University of Science & Technology, Wuhan, China 430074;Department of Computer Science, Huazhong University of Science & Technology, Wuhan, China 430074;Department of Computer Science, Huazhong University of Science & Technology, Wuhan, China 430074;Department of Computer Science, Huazhong University of Science & Technology, Wuhan, China 430074;Department of Electronic Engineering, City University of Hong Kong, Kowloon, China 999077
Venue:
Multimedia Tools and Applications
Year:
2009

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Abstract

In H.264/AVC, 4 脳 4 discrete cosine transform (DCT) is performed on the residual signals after intra prediction for decorrelation. Actually, residual blocks with different prediction modes exhibit different frequency characteristics. Therefore, the fixed transform matrix cannot match the energetic distribution of residual signals very well, which degrades the decorrelation performance. Fortunately, the energetic distributions of residual blocks with the same mode are relatively coincident, which makes it possible to train a universally good Karhunen---Loève transform (KLT) matrix for each mode. In this paper, an optimal frequency matching (OFM) algorithm is proposed to train KLT matrices for residual blocks and nine KLT matrices corresponding to nine prediction modes of 4 脳 4 intra blocks are trained. Experimental results show that KLT with trained matrices yields a persistent gain over H.264 using 4 脳 4 DCT with an average peak signal-to-noise ratio (PSNR) enhancement of 0.22dB and a maximum enhancement of 0.33dB.