.png){kind=icon}
Return
Conditional entropy-constrained residual VQ with application to image coding
.png){kind=icon}
OA
AI
DOI：10.1109/83.480766.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
This paper introduces an extension of entropy-constrained residual vector quantization (VQ) where intervector dependencies are exploited, The method, which we call conditional entropy-constrained residual VQ, employs a high-order entropy conditioning strategy that captures local information in the neighboring vectors, When applied to coding images, the proposed method is shown to achieve better rate-distortion performance than that of entropy-constrained residual vector quantization with less computational complexity and lower memory requirements. Moreover, it can be designed to support progressive transmission in a natural way, It is also shown to outperform some of the best predictive and finite-state VQ techniques reported in the literature, This is due partly to the joint optimization between the residual vector quantizer and a high-order conditional entropy coder as well as the efficiency of the multistage residual VQ structure and the dynamic nature of the prediction.
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
13.7
Papers:
1.0W
Citations:
8.4W
Organization
No organization information available
Cited Papers
.png){kind=icon}
ShareSaveShareSave
ShareSaveShareSaveShareSaveShareSave
View more