Channel-wise Feature Decorrelation for Enhanced Learned Image Compression
This work addresses the need for more efficient learned compression methods in image/video processing, offering a plug-and-play solution that is incremental but practical.
The paper tackles the problem of improving learned image compression efficiency without increasing computational complexity by proposing a channel-wise feature decorrelation loss to enhance latent feature diversity, achieving up to 8.06% BD-Rate improvement on established methods.
The emerging Learned Compression (LC) replaces the traditional codec modules with Deep Neural Networks (DNN), which are trained end-to-end for rate-distortion performance. This approach is considered as the future of image/video compression, and major efforts have been dedicated to improving its compression efficiency. However, most proposed works target compression efficiency by employing more complex DNNS, which contributes to higher computational complexity. Alternatively, this paper proposes to improve compression by fully exploiting the existing DNN capacity. To do so, the latent features are guided to learn a richer and more diverse set of features, which corresponds to better reconstruction. A channel-wise feature decorrelation loss is designed and is integrated into the LC optimization. Three strategies are proposed and evaluated, which optimize (1) the transformation network, (2) the context model, and (3) both networks. Experimental results on two established LC methods show that the proposed method improves the compression with a BD-Rate of up to 8.06%, with no added complexity. The proposed solution can be applied as a plug-and-play solution to optimize any similar LC method.