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Paper Detail

Paper IDCOM-1.5
Paper Title CONTEXT-ADAPTIVE SECONDARY TRANSFORM FOR VIDEO CODING
Authors Samruddhi Kahu, Madhu Krishnan, Xin Zhao, Shan Liu, Tencent America, United States
SessionCOM-1: Image and Video Coding
LocationArea H
Session Time:Tuesday, 21 September, 08:00 - 09:30
Presentation Time:Tuesday, 21 September, 08:00 - 09:30
Presentation Poster
Topic Image and Video Communications: Lossy coding of images & video
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract It is well-known that non-separable transforms can efficiently decorrelate arbitrarily directed textures that are often present in image and video content. Due to the computational complexity involved, it is usually applied as a secondary transform operating on low frequency primary transform coefficients. In order to represent a variety of arbitrary directional textures in natural images / videos, it is ideal to have sufficient coverage of secondary transform kernels for the codec to choose from. However, this may lead to increased signaling cost and encoder complexity. This paper proposes a context-adaptive secondary transform (CAST) kernel selection approach to enable the usage of more secondary transform kernels with no signaling cost increase and minimal encoder and decoder complexity increase. The proposed approach uses the variance of the top row and left column of reconstructed pixels adjacent to the transform block, if available, as a context for selecting the set of transform kernels. Experimental results show that, compared to libaom, the proposed algorithm achieves a luma BD-rate reduction of 2.17% and 3.11% for All Intra coding using PSNR and SSIM quality metrics, respectively.