.png){kind=icon}
.png){kind=icon}
Return
Causal Reversible Color Transform
.png){kind=icon}
PRE
AI
DOI：10.1109/LSP.2025.3626440.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Lossless image compression algorithms perform cross-channel decorrelation transformations, which are called reversible color transforms (RCTs), usually using integer lifting steps, which either require bit width inflated by one bit for the chroma channels or the use modular arithmetic that introduces wraparound artifacts, resulting from integer overflows. In this work we propose a fundamental technique that solves these problems entirely, by fusing the RCT with spatial prediction and adding causality to color domain, by predicting image data in a permuted RGB space. This method entirely avoids overflows, maintains reversibility, produces residuals with the same word size as the input, supports any kind of scan-line spatial predictors, and can be generalized to any number of channels. The proposed transformation is even guaranteed to improve coding efficiency, compared to all known fixed RCTs, without increasing computational cost or memory usage.
Keywords:
Image processing
lossless image compression
predictive coding
reversible color transform
Journal
I
IF:
3.9
Papers:
610
Citations:
0