.png){kind=icon}
.png){kind=icon}
Return
Near-Aperiodic Dot-Diffused Block Truncation Coding
.png){kind=icon}
PRE
AI
DOI：10.1016/j.sigpro.2015.09.030.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Block Truncation Coding (BTC) image compression technique is capable of extraordinary computational efficiency for high-framerate configurations under the trend of high image resolution. However, the recent development such as the Dot-Diffused BTC (DDBTC) method still encounters various challenges of visual artifacts, including impulsive noise, false contours and blocking effects. In this study, a new method, the Near-Aperiodic DDBTC (NADDBTC) technique, is proposed to analyze the tradeoff and balance between computational efficiency and visual artifacts. It is mainly because of the quantization levels and the co-optimized Class Matrix (CM) and Diffused Matrix (DM) during image compression procedure. In addition, the tiling method for bitmap generation is optimized for aperiodic compressed results. Experimental results show that this method is capable of excellent image quality and visual perception, as well as processing efficiency, at a level similar to the DDBTC technique by taking advantage of parallel processing in dot diffusion. (C) 2015 Elsevier B.V. All rights reserved.
Keywords:
Image compression
Block Truncation Coding
Dot diffusion
Digital halftoning
Optimization
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
3.6
Papers:
9.9K
Citations:
1.7W
