.png){kind=icon}
.png){kind=icon}
Return
Compression-Dependent Transform-Domain Downward Conversion for Block-Based Image Coding
.png){kind=icon}
PRE
AI
DOI：10.1109/TIP.2018.2806281.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Transform-domain downward conversion (TDDC) for image coding is usually implemented by discarding some high-frequency components from each transformed block. As a result, a block of fewer coefficients is formed, and a lower compression cost is achieved due to the coding of only a few low-frequency coefficients. In this paper, we focus on the design of a new TDDC-based coding method by using our proposed interpolation-compression directed filtering (ICDF) and error-compensated scalar quantization (ECSQ), leading to the compression-dependent TDDC (CDTDDC)-based coding. More specifically, ICDF is first used to convert each 16 x 16 macro-block into an 8x8 coefficient block. Then, this coefficient block is compressed with ECSQ, resulting in a smaller compression distortion for those pixels that locate at some specific positions of a macro-block. We select these positions according to the 4: 1 uniform sub-sampling lattice and use the pixels locating at them to reconstruct the whole macro-block through an interpolation. The proposed CDTDDC-based coding can be applied to compress both grayscale and color images. More importantly, when it is used in the color image compression, it offers not only a new solution to reduce the data-size of chrominance components but also a higher compression efficiency. Experimental results demonstrate that applying our proposed CDTDDC-based coding to compress still images can achieve a significant quality gain over the existing compression methods.
Keywords:
Image coding
sub-sampling
interpolation
distortion
quantization
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