Practical Learned Image Compression with Online Encoder Optimization

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.