Deep image compression with a unified spatial and channel context auto-regressive model

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2022-8

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Uludağ, Ali Sefkan

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Recently, variational auto-encoder-based compression models have gained much attention in learned image compression field due to their dimension reduction property. These models have three essential components which are encoder, decoder and entropy network. Encoder part of the VAE transforms a high dimensional space to a lower dimensional latent space, whereas the decoder serves as a reconstructing transformation. The entropy model is trained to generate the probability distributions of the latent representation to be used in arithmetic coding. End-to-end optimization is performed to minimize a rate-distortion loss composed of weighted sum of distortion loss of the decoder output and rate loss of the entropy model output. The VAE-based learned lossy image compression first introduced in Ballé (2016). Ballé (2018), improved this model by adapting a hyperprior network to increase the expression ability of spatial dependencies in the latent variable. Spatial auto-regressive and channel auto-regressive models are introduced in Minnen (2018) and Minnen (2020) respectively, to decrease the entropy of latent representation. This thesis presents a lossy image compression model which builds upon the aforementioned image compression architectures. It has been observed that spatial auto-regressive and channel auto-regressive models can be complementary and enhance the rate-distortion performance of the network. The channel auto-regressive model splits the latent representation along the channel dimension. It eliminates redundancies between channel slices by conditioning the slice being decoded to previously decoded slices. Spatial auto-regressive model captures the spatial correlations within a slice. Furthermore, it has been observed that the cumbersome spatial auto-regressive model can be parallelized by dividing the latent variables into smaller patches. Elements with the same index across patches are processed together, allowing parallel computation.

Subject Keywords

Image compression, Deep learning, Transform coding, Auto-encoder

URI

https://hdl.handle.net/11511/99476

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Graduate School of Natural and Applied Sciences, Thesis

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Citation Formats

A. S. Uludağ, “Deep image compression with a unified spatial and channel context auto-regressive model,” M.S. - Master of Science, Middle East Technical University, 2022.