End-to-end learned image compression with normalizing flows for latent space enhancement

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2022-9

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Yavuz, Fatih

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Learning based methods for image compression recently received considerable attention and demonstrated promising performance, surpassing many commonly used codecs. Architectures of learning based methodologies are typically comprised of a nonlinear analysis transform, which maps the input image to a latent representation, a synthesis transform that maps the quantized latent representation back to the image domain and a model for the probability distribution of the latent representation. Successful modelling of the probability distribution of the latent representation is critically important for their performance. Inspired by the success of normalizing flows as generative models, this work proposes a framework that utilizes flow based neural networks to improve the modelling of the probability distribution of the latent representation and consequently, the performance of a commonly known learned image compression network that is used as a benchmark. Normalizing flows implement an invertible mapping from one distribution to another, allowing the latent representation to be mapped to another domain in which its probability distribution can better match an intended probability distribution. The proposed networks are trained in an end-to-end fashion and can outperform the benchmark in rate-distortion performance.

Subject Keywords

Learned image compression, Invertible neural networks, Normalizing flows, Transform coding

URI

https://hdl.handle.net/11511/99780

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Graduate School of Natural and Applied Sciences, Thesis

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Citation Formats

F. Yavuz, “End-to-end learned image compression with normalizing flows for latent space enhancement,” M.S. - Master of Science, Middle East Technical University, 2022.