Linear Attention Modeling for Learned Image Compression

Recent years, learned image compression has made tremendous progress to achieve impressive coding efficiency. Its coding gain mainly comes from non-linear neural network-based transform and learnable entropy modeling. However, most of recent focuses have been solely on a strong backbone, and few studies consider the low-complexity design. In this paper, we propose LALIC, a linear attention modeling for learned image compression. Specially, we propose to use Bi-RWKV blocks, by utilizing the Spatial Mix and Channel Mix modules to achieve more compact features extraction, and apply the Conv based Omni-Shift module to adapt to two-dimensional latent representation. Furthermore, we propose a RWKV-based Spatial-Channel ConTeXt model (RWKV-SCCTX), that leverages the Bi-RWKV to modeling the correlation between neighboring features effectively, to further improve the RD performance. To our knowledge, our work is the first work to utilize efficient Bi-RWKV models with linear attention for learned image compression. Experimental results demonstrate that our method achieves competitive RD performances by outperforming VTM-9.1 by -14.84%, -15.20%, -17.32% in BD-rate on Kodak, Tecnick and CLIC Professional validation datasets.

Chanel-Orderer: A Channel-Ordering Predictor for Tri-Channel Natural Images

This paper shows a proof-of-concept that, given a typical 3-channel images but in a randomly permuted channel order, a model (termed as Chanel-Orderer) with ad-hoc inductive biases in terms of both architecture and loss functions can accurately predict the channel ordering and knows how to make it right. Specifically, Chanel-Orderer learns to score each of the three channels with the priors of object semantics and uses the resulting scores to predict the channel ordering. This brings up benefits into a typical scenario where an \texttt{RGB} image is often mis-displayed in the \texttt{BGR} format and needs to be corrected into the right order. Furthermore, as a byproduct, the resulting model Chanel-Orderer is able to tell whether a given image is a near-gray-scale image (near-monochromatic) or not (polychromatic). Our research suggests that Chanel-Orderer mimics human visual coloring of our physical natural world.