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Image compression with learned lifting-based DWT and learned tree-based entropy models

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Abstract

This paper explores learned image compression based on traditional and learned discrete wavelet transform (DWT) architectures and learned entropy models for coding DWT subband coefficients. A learned DWT is obtained through the lifting scheme with learned nonlinear predict and update filters. Several learned entropy models, with varying computational complexities, are explored to exploit inter- and intra-DWT subband coefficient dependencies, akin to traditional EZW, SPIHT, or EBCOT algorithms. Experimental results show that when the explored learned entropy models are combined with traditional wavelet filters, such as the CDF 9/7 filters, compression performance that far exceeds that of JPEG2000 can be achieved. When the learned entropy models are combined with the learned DWT, compression performance increases further. The computations in the learned DWT and all entropy models, except one, can be simply parallelized, and thus, the systems provide practical encoding and decoding times on GPUs, unlike other DWT-based learned compression systems in the literature.

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Data availability

The datasets that are analyzed during the current study (training and testing) are available from [39, 48], respectively.

Notes

  1. Available at https://github.com/uberkk/ImageCompressionLearnedLiftingandLearnedTreeBasedModels

  2. Encoding/decoding times for JPEG2000, and the two systems with IISCEM are obtained with a CPU (due to sequential encoding/decoding requirement), while all others are obtained with a GPU.

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Authors and Affiliations

  1. Electrical and Electronics Engineering, Middle East Technical University, 06800, Ankara, Turkey

    Ugur Berk Sahin & Fatih Kamisli

  2. Aselsan, Ankara, Turkey

    Ugur Berk Sahin

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  1. Ugur Berk Sahin
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  2. Fatih Kamisli
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Correspondence to Ugur Berk Sahin or Fatih Kamisli.

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The codes to repeat the results in this paper are available from the authors on GitHub [49].

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Communicated by Q. Shen.

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Appendix A

Appendix A

Following parameters are used in experimental results. In Fig. 7 a), \(ch=32\) is used for processing LL and \(ch=96\) is used for processing LH, HL, and HH subbands together. In Fig. 7 b), \(ch=32\) is used for each subband. In Fig. 8, \(ch=243\) is used for jointly processing LH, HL, and HH subbands, and output gives mean and scale for the corresponding three channels. In Fig. 9, on the right-hand side, \(ch=243\) is used for jointly processing LH, HL, and HH subbands and ch/3 is used on the left-hand side for processing each subband LH, HL, and HH separately (total of 243 channels). In Fig. 10, \(ch=162\) is used for processing each LH, HL, and HH subband separately. In Fig. 11, \(ch=81\) is used for processing each subband LL, LH, HL, and HH. In Fig. 13, \(ch=32\) is used. Our codes are available on github at [49].

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Sahin, U.B., Kamisli, F. Image compression with learned lifting-based DWT and learned tree-based entropy models. Multimedia Systems 29, 3369–3384 (2023). https://doi.org/10.1007/s00530-023-01192-w

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