Deep video coding has paved a way to break through the performance bottleneck of reigning hybrid video coding. However, unlike hybrid video codecs, existing deep video codecs cannot offer both flexible rates and regulable complexities within one single codec, which limits their applications. In this article, we propose a Regulable Deep Video Codec (RDVC) to address the above issue. First, we propose an Adaptive Feature Compression (AFC) network that generates variable rates while ensuring Rate-Distortion (RD) performance. The network introduces a two-stage coarse-to-fine rate adjustment that can be controlled by a user-specified rate level. Second, we propose a Spatio-Temporal Feature Propagation (STFP) mechanism to provide high-quality reference information for AFC process. Third, we also utilize slimmable convolutional components in our framework to adjust decoding complexity constrained by user configuration. Experimental results demonstrate that RDVC can adjust the codec structure flexibly according to different user configurations while maintaining advanced performance. On average, it reduces the bit-per-pixel (bpp) by 9.35%$/$58.12% while maintaining the same PSNR/MS-SSIM as the reference software VTM-13.2.

As video data continues to grow, the burden on network transmission increases significantly. Efficient video compression techniques are crucial to meet the rising demand for multimedia content. In this letter, we propose a Multi-scale Motion Alignment and Frame Reconstruction-based Video Codec (MFVC) for efficient video compression. MFVC focuses on optimizing the motion compensation and video reconstruction processes within a deep video compression framework. First, we design a Multi-Scale Motion Alignment Network (MSMA-Net) to achieve precise motion compensation, which extracts multi-scale features from video frames and utilizes flow information for deformable convolution. Second, we design a Frame Reconstruction Network (FR-Net) to recover high-quality video frames, which utilizes reference information for feature enhancement without additional bitrate consumption. Moreover, to achieve smooth rate adjustment, we introduce a feature scaling technique. Experimental results show that MFVC reduces bitrate by 7.86%/48.34% compared to VVC (VTM 13.2) at the same PSNR/MS-SSIM.