Geometric Transformation-Embedded Mamba for Learned Video Compression
This work addresses the complexity of existing learned video compression methods for researchers and practitioners by offering a streamlined, direct transform approach.
This paper proposes a direct transform strategy for learned video compression, moving away from hybrid coding paradigms. The method utilizes a cascaded Mamba module with geometric transformations and a locality refinement feed-forward network, achieving superior perceptual quality and temporal consistency compared to state-of-the-art approaches under low-bitrate constraints.
Although learned video compression methods have exhibited outstanding performance, most of them typically follow a hybrid coding paradigm that requires explicit motion estimation and compensation, resulting in a complex solution for video compression. In contrast, we introduce a streamlined yet effective video compression framework founded on a direct transform strategy, i.e., nonlinear transform, quantization, and entropy coding. We first develop a cascaded Mamba module (CMM) with different embedded geometric transformations to effectively explore both long-range spatial and temporal dependencies. To improve local spatial representation, we introduce a locality refinement feed-forward network (LRFFN) that incorporates a hybrid convolution block based on difference convolutions. We integrate the proposed CMM and LRFFN into the encoder and decoder of our compression framework. Moreover, we present a conditional channel-wise entropy model that effectively utilizes conditional temporal priors to accurately estimate the probability distributions of current latent features. Extensive experiments demonstrate that our method outperforms state-of-the-art video compression approaches in terms of perceptual quality and temporal consistency under low-bitrate constraints. Our source codes and models will be available at https://github.com/cshw2021/GTEM-LVC.