Residual Learning and Filtering Networks for End-to-End Lossless Video Compression
This work addresses video compression challenges for applications requiring efficient storage and transmission, but it is incremental as it builds on existing learning-based methods with specific architectural improvements.
The paper tackles inaccurate motion estimation and inadequate motion compensation in learning-based video compression by proposing an end-to-end method with residual skip connections, filtering networks, and nonlinear transforms, achieving competitive performance on datasets like HEVC, UVG, VTL, and MCL-JCV.
Existing learning-based video compression methods still face challenges related to inaccurate motion estimates and inadequate motion compensation structures. These issues result in compression errors and a suboptimal rate-distortion trade-off. To address these challenges, this work presents an end-to-end video compression method that incorporates several key operations. Specifically, we propose an autoencoder-type network with a residual skip connection to efficiently compress motion information. Additionally, we design motion vector and residual frame filtering networks to mitigate compression errors in the video compression system. To improve the effectiveness of the motion compensation network, we utilize powerful nonlinear transforms, such as the Parametric Rectified Linear Unit (PReLU), to delve deeper into the motion compensation architecture. Furthermore, a buffer is introduced to fine-tune the previous reference frames, thereby enhancing the reconstructed frame quality. These modules are combined with a carefully designed loss function that assesses the trade-off and enhances the overall video quality of the decoded output. Experimental results showcase the competitive performance of our method on various datasets, including HEVC (sequences B, C, and D), UVG, VTL, and MCL-JCV. The proposed approach tackles the challenges of accurate motion estimation and motion compensation in video compression, and the results highlight its competitive performance compared to existing methods.