Enhanced JPEG2000 Quality Scalability through Block-Wise Layer Truncation

Abstract

Quality scalability is an important feature of image and video coding systems. In JPEG2000, quality scalability is achieved through the use of quality layers that are formed in the encoder through rate-distortion optimization techniques. Quality layers provide optimal rate-distortion representations of the image when the codestream is transmitted and/or decoded at layer boundaries. Nonetheless, applications such as interactive image transmission, video streaming, or transcoding demand layer fragmentation. The common approach to truncate layers is to keep the initial prefix of the to-be-truncated layer, which may greatly penalize the quality of decoded images, especially when the layer allocation is inadequate. So far, only one method has been proposed in the literature providing enhanced quality scalability for compressed JPEG2000 imagery. However, that method provides quality scalability at the expense of high computational costs, which prevents its application to the aforementioned applications. This paper introduces a Block-Wise Layer Truncation (BWLT) that, requiring negligible computational costs, enhances the quality scalability of compressed JPEG2000 images. The main insight behind BWLT is to dismantle and reassemble the to-be-fragmented layer by selecting the most relevant codestream segments of codeblocks within that layer. The selection process is conceived from a rate-distortion model that finely estimates rate-distortion contributions of codeblocks. Experimental results suggest that BWLT achieves near-optimal performance even when the codestream contains a single quality layer.