Directional graph weight prediction for image compression

Abstract

Graph-based models have recently attracted attention for their potential to enhance transform coding image compression thanks to their capability to efficiently represent discontinuities. Graph transform gets closer to the optimal KLT by using weights that represent inter-pixel correlations but the extra cost to provide such weights can overwhelm the gain, especially in the case of natural images rich of details. In this paper we provide a novel idea to make graph transform adaptive to the actual image content, avoiding the need to encode the graph weights as side information. We show that an approach similar to spatial prediction can be used to effectively predict graph weights in place of pixels; in particular, we propose the design of directional graph weight prediction modes and show the resulting coding gain. The proposed approach can be used jointly with other graph based intra prediction methods to further enhance compression. Our comparative experimental analysis, carried out with a fully fledged still image coding prototype, shows that we are able to achieve significant coding gains.

Graph Spectral Image Processing

“…More sophisticated graph coding techniques may also be devised, e.g. one might in principle apply contour coding techniques as in [52], [53] to reduce the cost of representing the graph. Moreover, in [53] directional graph weight prediction modes are proposed, which avoid transmitting any overhead information to the decoder.…”

Graph Laplacian for image anomaly detection

“…In particular, spectral graph theory has been recently bridged with signal processing, where the graph is used to model local relations between signal samples [57,60]. As an example, graph-based signal processing is emerging as a novel approach in the design of energy compacting image transformations [27,28,39,64,70].…”