Image compression methods generally can be divided into two categories, quality loss and quality lossless. However, the first method is not suitable for this situation due to the reason that label information of steel plate ought to be clear, accurate and intact. The lossless compression methods utilize number of different algorithms including Huffman coding, Run-length encoding, dictionary-based compression, arithmetic coding, plus.
Papers:
Yong-dong Wang, Dong-wei Xu , Yun Lu, Jun-Yan Shen, Gui-jun Zhang, “Compression algorithm of road traffic data in time series based on temporal correlation,” IET Intelligent Transport Systems, vol. 12, no. 3, pp. 177-185, 2018.
M.Rajasekhar Reddy,K.Akshaya, R. Alice Infanta Seles, RA.Dhivya, K.S.Ravichandran, “Image Compression using ShannonFano-Elias Coding and Run Length Encoding,” 2018 Second International Conference on Inventive Communication and Computational Technologies (ICICCT) , 2018.
Marcelino Rodriguez-Cancio, Jules White, Bneoit Baudry, “Images of Code: Lossy Compression for Native Instructions,” 2018 ACM/IEEE 40th International Conference on Software Engineering: New Ideas and Emerging Results, 2018.
Wei Wang; Wei Zhang, “Huffman Coding-Based Adaptive Spatial Modulation,” IEEE Transactions on Wireless Communications, vol. 16, no. 8, pp. 5090-5101, 2017.
Komal Sharma, Kunal Gupta, “Lossless Data Compression Techniques and Their Performance”, 2017 International Conference on Computing, Communication and Automation (ICCCA), 2017.
Well, since nobody answers...
I think the only performance metric is the achieved compression-ratio.
Very secondary comes execution-time for compressing and decompressing. And lastly: how complicated is it, does it need a big library of code.
Beside compression ratio, compression efficiency, bit per pixel and bit per voxel (for volumetric compression) are the other lossless compression metrics that can be used for RLE and other lossless compression algorithms.
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