.png){kind=icon}
返回
BlinQS: Blind quality scalable image compression algorithm without using PCRD optimization
.png){kind=icon}
PRE
AI
DOI：10.1007/s11042-023-15454-2.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
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 i.e., transcoding algorithm through a normal distribution function 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.
Keyword:
Blind quality scalability (BlinQS)
Image compression
Rate-distortion optimization
JPEG-2000 standard
期刊
IF:
3
论文数:
2.0W
被引数:
3.2W
机构
引用论文
Target-Dependent Scalable Image Compression Using a Reconfigurable Recurrent Neural Network.png)
IEEE ACCESS
OAAI
IEEE ACCESSIF3.6
OAAI分享收藏分享收藏分享收藏
查看更多内容