  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.
