Online Graph Based Transforms for Intra-Predicted Imaging Data

Abstract

Orthogonal transforms are key components of several image and video compression systems and standards, as they provide a de-correlated representation of signals to enhance compressibility. However, the most commonly used transforms for compression, such as the Discrete Cosine transforms (DCT) and Discrete Sine transforms (DST), are fixed and non-adaptive, limiting their ability to capture complex or varying signal characteristics. Graph-based transforms (GBTs) have shown improved energy compaction and reconstruction performance, but face two major limitations: the need to signal graph information in the compressed bitstream, which increases overhead and may complicates decoder synchronization, and a dependency on offline training process, which is highly dependent on the quality and completeness of the training data. To address these issues, this paper introduces a novel framework, GBT-ONL, which learns GBTs online in the context of block-based predictive transform coding. The proposed GBT-ONL framework uses a shallow fully connected neural network to predict the graph Laplacian needed for both the forward and inverse GBT. By relying only on information available during encoding, GBT-ONL eliminates the need to signal additional information in the compressed bitstream, and removes the requirement for any prior offline training. Evaluations on several video sequences show that GBT-ONL outperforms both traditional (non-learnable) transforms and existing learnable transforms in terms of energy compaction, reconstruction error, and compression efficiency, as measured by BD-PSNR and BD-Rate metrics.