Quantization-free Lossy Image Compression Using Integer Matrix Factorization

• URL: http://arxiv.org/abs/2408.12691v1
• Date: Thu, 22 Aug 2024 19:08:08 GMT
• Title: Quantization-free Lossy Image Compression Using Integer Matrix Factorization
• Authors: Pooya Ashtari, Pourya Behmandpoor, Fateme Nateghi Haredasht, Jonathan H. Chen, Panagiotis Patrinos, Sabine Van Huffel,
• Abstract summary: We introduce a variant of integer matrix factorization (IMF) to develop a novel quantization-free lossy image compression method. IMF provides a low-rank representation of the image data as a product of two smaller factor matrices with bounded integer elements. Our method consistently outperforms JPEG at low bit rates below 0.25 bits per pixel (bpp) and remains comparable at higher bit rates.
• Score: 8.009813033356478
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Lossy image compression is essential for efficient transmission and storage. Traditional compression methods mainly rely on discrete cosine transform (DCT) or singular value decomposition (SVD), both of which represent image data in continuous domains and therefore necessitate carefully designed quantizers. Notably, SVD-based methods are more sensitive to quantization errors than DCT-based methods like JPEG. To address this issue, we introduce a variant of integer matrix factorization (IMF) to develop a novel quantization-free lossy image compression method. IMF provides a low-rank representation of the image data as a product of two smaller factor matrices with bounded integer elements, thereby eliminating the need for quantization. We propose an efficient, provably convergent iterative algorithm for IMF using a block coordinate descent (BCD) scheme, with subproblems having closed-form solutions. Our experiments on the Kodak and CLIC 2024 datasets demonstrate that our IMF compression method consistently outperforms JPEG at low bit rates below 0.25 bits per pixel (bpp) and remains comparable at higher bit rates. We also assessed our method's capability to preserve visual semantics by evaluating an ImageNet pre-trained classifier on compressed images. Remarkably, our method improved top-1 accuracy by over 5 percentage points compared to JPEG at bit rates under 0.25 bpp. The project is available at https://github.com/pashtari/lrf .

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