DCM-VideoNet: A Densely-Connected Modulated Decoder Framework for Implicit Neural Video Compression
Cih-Wei Wong, Hsu-Feng Hsiao · 2025
We introduce DCM-VideoNet, a novel implicit neural representation that enhances video reconstruction and compression through Densely-Connected Modulated Decoder (DCMD) blocks, enabling efficient feature fusion and robust representation learning. By incorporating Compact Inverted Bottleneck (CIB) structures, these decoders achieve better parameter efficiency without compromising expressive capability. Integrated modulation mechanisms further refine feature extraction, leading to improved reconstruction fidelity. To address the spectral bias commonly observed in neural networks, we propose a Spectral Bias Mitigation Loss (SBM Loss) with adaptive frequency weighting, ensuring a balanced evaluation of reconstruction quality during training. For video compression, our method employs a comprehensive strategy that includes layer-wise pruning, quantization-aware training, and arithmetic coding to realize efficient model compression. Extensive experiments demonstrate that DCM-VideoNet not only outperforms state-of-the-art implicit neural representation techniques but also achieves competitive performance relative to leading learning-based video compression methods.