Image compression method based on learned lifting-based dwt and learned zerotree-like entropy model

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2022-8

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Şahin, Uğur Berk

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The success of deep learning in computer vision has sparked great interest in investigating deep learning-based algorithms also in many image processing applications, including image compression. The most popular end-to-end learned image compression approaches are based on auto-encoder architectures, where the image is mapped via convolutional neural networks (CNNs) into a transform (latent) representation that is quantized and processed again with CNNs to obtain the reconstructed image. The quantized latent representation is entropy coded to obtain a compressed bitstream. To have efficient entropy coding, the probability distribution of the quantized latent representation is also modeled with CNNs. The entire system, including the auto-encoder and the probability model of the latent representation, is trained jointly to minimize the rate-distortion cost function.

Subject Keywords

Image compression, Jpeg2000, Neural network, EZWT, Lifting structure, Cnn

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https://hdl.handle.net/11511/98622

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Graduate School of Natural and Applied Sciences, Thesis

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Citation Formats

U. B. Şahin, “Image compression method based on learned lifting-based dwt and learned zerotree-like entropy model,” M.S. - Master of Science, Middle East Technical University, 2022.