Channel-wise Feature Decorrelation for Enhanced Learned Image
  Compression
Farhad Pakdaman Moncef Gabbouj
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.