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

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.