JOURNAL ARTICLE
47‐4: A Novel Demura Compensation Data Compression Algorithm based on JPEG‐LS.
Published In: SID Symposium Digest of Technical Papers, 2024, v. 55, n. 1. P. 634 1 of 3
Database: Applied Science & Technology Source Ultimate 2 of 3
Authored By: Chen, Lin; Wang, Shuaizhao; Guo, Xingling; Tan, Xiaoping; Huang, Jiting; Ge, Mingwei; Zhu, Xiujian 3 of 3
Abstract
JPEG‐LS is a lossless/near‐lossless image compression algorithm based on context modeling, which has the advantages of easy implementation, low resource consumption and high compression rate. It is widely used in the compression field of continuous‐tone still images. However, single image compression has significant latency and high hardware resource consumption issues, as the JPEG‐LS algorithm requires pixel by pixel prediction and real‐time context updates during the compression process, which is not conducive to algorithm IP implementation. Based on Demura compensation data compression requirements, this article has made changes to the pixel prediction method of the JPEG‐LS algorithm and delayed the update of the context to solve the compression latency and resource consumption issues. After algorithm optimization (NJPEGLS), the linebuffer resource occupation was reduced by 8/9, the clock frequency reached above 140 MHz, and the comprehensive compression rate loss was within 4.5%, meeting the demand for Demura compensation data compression/decompression. In this paper, we also conducted statistics and analysis on the distribution of values, and found that the distribution of values was very regular, and we propose an adaptive value scheme (KNJPEG‐LS) to further simplify the hardware circuit. [ABSTRACT FROM AUTHOR]
Additional Information
- Source:SID Symposium Digest of Technical Papers. 2024/06, Vol. 55, Issue 1, p634
- Document Type:Article
- Subject Area:Computer Science
- Publication Date:2024
- ISSN:0097966X
- DOI:10.1002/sdtp.17604
- Accession Number:178715418
- Copyright Statement:Copyright of SID Symposium Digest of Technical Papers is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Looking to go deeper into this topic? Look for more articles on EBSCOhost.