QARV: Quantization-Aware ResNet VAE for Lossy Image Compression

By Zhihao Duan, Ming-Tse Lu, Jackie Ma, Zhan Ma, Feng Zhu. 2023

99 citations recorded by the source corpus.

This paper addresses the problem of lossy image compression, a fundamental problem in image processing and information theory that is involved in many real-world applications. We start by reviewing the framework of variational autoencoders (VAEs), a powerful class of generative probabilistic models that has a deep connection to lossy compression. Based on VAEs, we develop a new scheme for lossy image compression, which we name quantization-aware ResNet VAE (QARV). Our method incorporates a hierarchical VAE architecture integrated with test-time quantization and quantization-aware training, without which efficient entropy coding would not be possible. In addition, we design the neural network architecture of QARV specifically for fast decoding and propose an adaptive normalization operation for variable-rate compression. Extensive experiments are conducted, and results show that QARV achieves variable-rate compression, high-speed decoding, and better rate-distortion performance than existing baseline methods.

Subfields: Computer Vision and Pattern Recognition

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In Computer Vision and Pattern Recognition

  1. Zhihao Duan
  2. Justin Yang
  3. Zhan Ma
  4. F. Zhu
  5. Jiangpeng He
  6. Ming-Tse Lu

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