2024/02/26 by Wen-Yang Lu, Lu, Wen-Yang, Eduardo Pavéz +7
Computer Science · #FOS: Electrical engineering #Image and Video Processing (eess.IV) #Text and Document Classification Technologies #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2402.16371
openalex publication_date 2024/02/26 · openalex created_date 2024/02/28 · openalex updated_date 2026/07/28
Current video coding standards, including H.264/AVC, HEVC, and VVC, employ discrete cosine transform (DCT), discrete sine transform (DST), and secondary to Karhunen-Loeve transforms (KLTs) decorrelate the intra-prediction residuals. However, the efficiency of these transforms in decorrelation can be limited when the signal has a non-smooth and non-periodic structure, such as those occurring in textures with intricate patterns. This paper introduces a novel adaptive separable path graph-based transform (GBT) that can provide better decorrelation than the DCT for intra-predicted texture data. The proposed GBT is learned in an online scenario with sequential K-means clustering, which groups similar blocks during encoding and decoding to adaptively learn the GBT for the current block from previously reconstructed areas with similar characteristics. A signaling overhead is added to the bitstream of each coding block to indicate the usage of the proposed graph-based transform. We assess the performance of this method combined with H.264/AVC intra-coding tools and demonstrate that it can significantly outperform H.264/AVC DCT for intra-predicted texture data.