Intra-Predictive Transforms for Block-Based Image Coding
This paper presents the theory and the design of intra-predictive transforms, which unify the inter-block prediction and block-based transforms in block-based image coding. Motivated by interpreting inter-block prediction as a transform with a larger size, we derive the concept of intra-predictive transforms. Conventional predictions and transforms can be viewed as special cases of intra-predictive transforms. Intra-predictive transforms are able to exploit both inter and intra-block correlations. We derive the tight upper bound of the coding gain of intra-predictive transforms for stationary Gaussian sources. It turns out that the coding gain can be greater than that of conventional transforms. The optimal intra-predictive transform that achieves the upper bound is also derived. We also design a practical intra-predictive transform using frequency-domain prediction that can achieve better performance in image coding while exhibiting low computational complexity. Experimental results confirm the effectiveness of the proposed intra-predictive transforms in block-based image coding systems and show the improvements over the current design.