Regression-based Intra-prediction for Image and Video Coding
This work addresses coding efficiency for image and video compression applications, representing an incremental improvement over existing standards.
The paper tackles the problem of improving intra-prediction in image and video coding by developing predictors derived from natural image blocks, resulting in significant improvements in estimation quality over designed predictors like HEVC across all conditions while maintaining reasonable computational complexity.
By utilizing previously known areas in an image, intra-prediction techniques can find a good estimate of the current block. This allows the encoder to store only the error between the original block and the generated estimate, thus leading to an improvement in coding efficiency. Standards such as AVC and HEVC describe expert-designed prediction modes operating in certain angular orientations alongside separate DC and planar prediction modes. Being designed predictors, while these techniques have been demonstrated to perform well in image and video coding applications, they do not necessarily fully utilize natural image structures. In this paper, we describe a novel system for developing predictors derived from natural image blocks. The proposed algorithm is seeded with designed predictors (e.g. HEVC-style prediction) and allowed to iteratively refine these predictors through regularized regression. The resulting prediction models show significant improvements in estimation quality over their designed counterparts across all conditions while maintaining reasonable computational complexity. We also demonstrate how the proposed algorithm handles the worst-case scenario of intra-prediction with no error reporting.