Feihong Mei

University of Science and Technology of China

Author Statistics

Papers

Citation

H-Index

i-10 index

Research Trends

Author Order

Document Type

Co-Authors

Dong Liu

University of Science and Technology of China

Houqiang Li

University of Science and Technology of China

Junqi Liao

University of Science and Technology of China

Haotian Zhang

University of Science and Technology of China

Li Li

Sichuan Agricultural University

Li Li

University of Science and Technology of China

Cooperative Institutions

University of Science and Technology of China

Chinese Academy of Sciences

Affiliated Hospital of Nantong University

Nantong University

Fujian Provincial Hospital

Fujian Medical University

Shanghai Center for Brain Science and Brain-Inspired Technology

Dalian Medical University

National Science Center

Institute of Art

Author Statistics

Papers

Citation

H-Index

i-10 index

Research Field

Variable-rate Learned Image Compression with Adaptive Quantization Step Size

2022 IEEE International Conference on Visual Communications and Image Processing (VCIP) (2023)

Feihong Mei Li Li Dong Liu

Nowadays, learned image compression has outperformed traditional coding methods like VVC. However, learned image compression methods are often optimized for specific rates and lack support for variable rate compression. Though some efforts have been made to address variable rate compression, striking the right balance between performance and complexity remains a challenge. To address this, we propose the Adaptive Quantize Step Size (AQS) technique, allowing continuous variable rate within a single model. Unlike methods that learn to scale the latent representation, AQS enables learning of different quantization step sizes for different rates, preserving the latent representation's distribution while only affecting the quantization step size, which is beneficial for the entropy model. As a result, our method achieves superior performance compared to existing methods while maintaining similar complexity. It delivers comparable results across a wide range of rates using a single model with minimal additional parameters.

Data compression ratio

10.1109/vcip59821.2023.10402651

Cite

Citations (0)

Practical Learned Image Compression with Online Encoder Optimization

Haotian Zhang Feihong Mei Junqi Liao Li Li Houqiang Li

Learned image compression methods have shown significant advances in performance. However, they often suffer from higher decoding complexity compared to traditional codecs. In this paper, we present an approach toward practical learned image compression that focuses on faster decoding by employing online encoder optimization. To reduce network complexity, we employ a hyperprior structure with smaller convolution kernels, which enables efficient compression. We also introduce a content adaptive skipping algorithm to accelerate entropy decoding. By jointly optimizing entropy decoding complexity and the distortion of the reconstructed image at the encoder side, we achieve faster decoding while maintaining the quality of the reconstructed image. Furthermore, we incorporate online iterative optimization at the encoder side to enhance rate-distortion performance without increasing decoding complexity. This approach allows us to fine-tune the latent and improve overall performance. Our method achieves a notable improvement over BPG on the VCIP 2023 challenge test set, with more than 1 dB increase in PSNR and a 50% reduction in decoding time.

10.1109/pcs60826.2024.10566341

Cite

Citations (0)