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Published on 31 July 2024
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Mo,H. (2024). A multi-predictor based lossless ARGB texture compression algorithm and FPGA implementation. Advances in Engineering Innovation,9,62-68.
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A multi-predictor based lossless ARGB texture compression algorithm and FPGA implementation

Handong Mo *,1,
  • 1 South China Agricultural University

* Author to whom correspondence should be addressed.

https://doi.org/10.54254/2977-3903/9/2024093

Abstract

. In the fields of GPU and AI chip design, the frequent read and write operations on the color buffer data (ARGB), which are intensive in graphical and image access, significantly impact performance. There is a need for applications that require random access and only read small images once. To address this situation, this paper proposes an algorithm with lower modeling complexity, yet achieving near-complex implementation results, along with its FPGA implementation method. Through actual testing on multiple images, the average lossless compression rate reached 40.3%. With hardware acceleration, the execution efficiency of the algorithm was further improved, ensuring both compression rate and speed, thus confirming the effectiveness of the algorithm.

Keywords

lossless compression, image compression, texture compression, FPGA

[1]. Yin, M., & Sun, G. (2024). FPGA design of a lossless ARGB data compression and decompression algorithm. Computer Measurement & Control, 32(02), 317-324. https://doi.org/10.16526/j.cnki.11-4762/tp.2024.02.045

[2]. Chen, D., Yu, M., Dai, M., et al. (2020). Lossless image compression of acyclic graphs based on variable bit-rate coding. Control Engineering, 27(05), 812-818. https://doi.org/10.14107/j.cnki.kzgc.20190452

[3]. Jiang, H., & Zhou, X. (2003). Lossless image compression based on local texture features. Journal of Beijing University of Aeronautics and Astronautics, (06), 505-508. https://doi.org/10.13700/j.bh.1001-5965.2003.06.009

[4]. Weinberger, M. J., Seroussi, G., & Sapiro, G. (2000). The LOCO-I lossless image compression algorithm: principles and standardization into JPEG-LS. IEEE Transactions on Image Processing, 9(8), 1209-1224.

[5]. Sheikh, H. R., Bovik, A. C., & Cormack, L. (2005). No-reference quality assessment using natural scene statistics: JPEG2000. IEEE Transactions on Image Processing, 14(11), 1918-1927.

[6]. Wu, X., & Memon, N. (2000). Context-based lossless interband compression--extending CALIC. IEEE Transactions on Image Processing, 9(6), 994-1001.

[7]. Wu, X. (1997). Lossless compression of continuous-tone images via context selection, quantization, and modeling. IEEE Transactions on Image Processing, 6(5), 656-664.

[8]. Wu, X. (2017). Research on arithmetic coding algorithm in image compression. Computer and Digital Engineering, 45(09), 1863-1865.

[9]. Wang, C., & Wang, J. (2004). Data compression method based on energy threshold and adaptive arithmetic coding. Power System Automation, (24), 56-60.

Cite this article

Mo,H. (2024). A multi-predictor based lossless ARGB texture compression algorithm and FPGA implementation. Advances in Engineering Innovation,9,62-68.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Journal:Advances in Engineering Innovation

Volume number: Vol.9
ISSN:2977-3903(Print) / 2977-3911(Online)

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