BiCRVC: An Efficient Bidirectional Neural Video Compression Framework via Coupled Representation Coding

Neural video compression (NVC) has achieved strong compression performance, but practical random-access coding still faces two technical challenges: existing bidirectional NVCs (BVCs) usually require costly motion-first decoding, and reliable motion estimation is difficult under long-range bidirectional prediction. To address these issues, we present BiCRVC, an efficient bidirectional neural video compression framework based on coupled representation coding. Instead of coding motion and frame information with two separate codecs, BiCRVC transforms the motion representation and the current-frame latent into a unified latent representation for entropy coding. This design enables motion and frame information to be decoded from the same bitstream with one unified codec, while still reconstructing motion-aligned contexts for frame decoding. To improve motion accuracy, we introduce multi-candidate motion estimation (MCME), which combines multi-scale motion estimation and parallel accumulated motion estimation to better handle diverse and long-range motions. To reduce motion coding overhead, we further propose bidirectional motion feature propagation (BMFP), which reuses previously decoded motion features at both the encoder and decoder as temporal priors for conditional motion coding. In addition, coupled distortion training and random GOP structure training are used to encourage joint motion-frame coding and improve adaptation to hierarchical random-access structures. Experiments show that BiCRVC achieves better compression performance than state-of-the-art BVCs while providing about 30 times faster 1080p decoding than recent BVCs.