Scalable image coding based on epitomes

TL;DR

This paper introduces a scalable image coding scheme utilizing epitomes, which are compact representations of images, and employs local learning-based super-resolution techniques to enhance image quality.

Contribution

It presents a novel epitome-based scalable coding method that improves rate-distortion performance by restoring missing pixels with learned local models.

Findings

Significant rate-distortion improvements over SHVC reference.

Effective use of local learning for image enhancement.

Two approaches for pixel restoration demonstrate versatility.

Abstract

In this paper, we propose a novel scheme for scalable image coding based on the concept of epitome. An epitome can be seen as a factorized representation of an image. Focusing on spatial scalability, the enhancement layer of the proposed scheme contains only the epitome of the input image. The pixels of the enhancement layer not contained in the epitome are then restored using two approaches inspired from local learning-based super-resolution methods. In the first method, a locally linear embedding model is learned on base layer patches and then applied to the corresponding epitome patches to reconstruct the enhancement layer. The second approach learns linear mappings between pairs of co-located base layer and epitome patches. Experiments have shown that significant improvement of the rate-distortion performances can be achieved compared to an SHVC reference.