Image lossless compression algorithm optimization and FPGA implementation

Fangbin Dang

doi:10.54097/fcis.v3i2.7194

Authors

Fangbin Dang

DOI:

https://doi.org/10.54097/fcis.v3i2.7194

Keywords:

Image lossless compression, Compression ratio, Time complexity, FPGA

Abstract

In this thesis, a minimum redundant prefix coding with higher compression ratio and lower time complexity is proposed for lossless compression of HD images. The compression algorithm is based on Canonical Huffman coding, preprocesses the data source to be compressed according to the image data features, and then compresses the data in batches using the locally uneven features in the data, which improves the compression ratio by 1.678 times compared with the traditional canonical Huffman coding. During the implementation of the algorithm, the counting sorting method with lower time complexity and the code-length table construction method without relying on binary trees are used to reduce the complexity of the algorithm and achieve the purpose of real-time data processing by the system. Finally, the proposed compression algorithm is deployed in FPGA to improve the encoding rate by parallel hardware circuit and pipeline design.

Downloads

Download data is not yet available.

References

Bin W E N, Minghua H E, Minqi H. AVS Exponential-Golomb Code Algorithm[J]. Computer Engineering, 2011, 37(15): 218-220.

Wernik C, Ulacha G, Ieee. Application of adaptive Golomb codes for lossless audio compression[C]. Signal Processing - Algorithms, Architectures, Arrangements, and Applications (SPA), 2018: 203-207.

Hashempour H, Lombardi F. Application of arithmetic coding to compression of VLSI test data[J]. Ieee Transactions on Computers, 2005, 54(9): 1166-1177.

Dyer M, Taubman D, Nooshabadi S, et al. Concurrency techniques for arithmetic coding in JPEG2000[J]. Ieee Transactions on Circuits and Systems I-Regular Papers, 2006, 53(6): 1203-1213.

Dube D, Yokoo H, Ieee. Fast Construction of Almost Optimal Symbol Distributions for Asymmetric Numeral Systems[C]. IEEE International Symposium on Information Theory (ISIT), 2019: 1682-1686.

Wang N, Wang C, Lin S-J. A simplified variant of tabled asymmetric numeral systems with a smaller look-up table[J]. Distributed and Parallel Databases, 2021, 39(3): 711-732.

Patel R, Kumar V, Tyagi V, et al. A Fast and Improved Image Compression Technique Using Huffman Coding[C]. IEEE International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET), 2016: 2283-2286.

Kasban H, Hashima S. Adaptive Radiographic Image Compression Technique using Hierarchical Vector Quantization and Huffman Encoding[J]. Journal of Ambient Intelligence and Humanized Computing, 2019, 10(7): 2855-2867.