BlinQS: Blind Quality Scalable Image Compression Algorithm without using PCRD Optimization

TL;DR

BlinQS introduces a novel, computationally efficient image compression algorithm that achieves quality scalability without PCRD optimization, outperforming JPEG-2000 in single-layer scenarios and closely matching its performance.

Contribution

It proposes a PCRD-free, blind quality scalable image compression method that reduces computational complexity and maintains high perceptual quality.

Findings

Outperforms JPEG-2000 in single quality layer compression.

Achieves results close to JPEG-2000 without PCRD optimization.

Reduces computational complexity in quality scalability.

Abstract

Quality Scalability is one of the important features of interactive imaging to obtain better perceptual quality at a specified target bit rate. In JPEG 2000, it is achieved using quality layers obtained by Rate-Distortion (R-D) optimization techniques in Tier-II coding. Some important concerns here are: (i) inefficient conventional Post-Compression Rate-Distortion (PCRD) optimization algorithms, (ii) lack of quality scalability for less or single quality layer string. This paper takes the above mentioned concerns into account and proposes a Blind Quality Scalable (BlinQS) algorithm that provides scalability with the least computational complexity. The novel part of this method is to eliminate the Tier-II coding and add a blind string selection algorithm through a normal distribution for efficient rate control. The results obtained suggest that the proposed method achieves better results than JPEG-2000 at single quality layer and achieves results close to JPEG-2000 without using PCRD optimization algorithms.