A pixel-by-pixel learned lossless image compression method with parallel decoding

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2022-7

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Gümüş, Sinem

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The success of deep learning in computer vision applications has led to the use of learning based algorithms also in image compression. Learning based lossless image compression algorithms can be divided into three categories, namely, pixel-by-pixel (or masked convolution based) algorithms, prior based algorithms and latent representation based algorithms. In the pixel-by-pixel algorithms, each pixel’s probability distribution is obtained by processing the previously coded left and upper neighbouring pixels with a neural network (NN), which is then used by an arithmetic coder for lossless compression. In the prior based algorithms, the probability distribution of the image is conditioned on a prior that is obtained with a NN and transmitted to the decoder. In the latent representation based algorithms, the image is transformed to a latent domain with a learned invertible mapping, and the latent representation is lossless compressed. This thesis studies a learned lossless image compression method that falls into the pixel-by-pixel (or masked convolution based) algorithms category. The study aims to provide a learned lossless image compression method by modelling each pixel’s probability distribution with a Gaussian Mixture Model (GMM), whose parameters are obtained by processing the pixel’s causal neighbourhood (i.e. previously compressed pixels) with a relatively simple NN. This causality dependency causes the decoder to operate sequentially, i.e. the NN has to be run for each pixel sequentially, which increases decoding time significantly. The causality dependency can be easily alleviated at the encoder via masked convolutions. To reduce the decoding time, parallel encoding and decoding algorithms are studied and implemented. The obtained lossless image compression performance is competitive and is compared to both state-of-the art traditional and learning based methods.

Subject Keywords

Lossless image compression, Conditional probability model, Artificial neural networks (ANN)

URI

https://hdl.handle.net/11511/98180

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Graduate School of Natural and Applied Sciences, Thesis

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Citation Formats

S. Gümüş, “A pixel-by-pixel learned lossless image compression method with parallel decoding,” M.S. - Master of Science, Middle East Technical University, 2022.