Res-NeRV: Residual Blocks For A Practical Implicit Neural Video Decoder

This paper proposes the integration of residual blocks into neural representation for videos (NeRV)-based architectures with the aim of enhancing the reconstruction of detailed patterns and high-level features. Additionally, a coding pipeline is introduced, placing the implicit neural decoder in a real-life video streaming framework. Indeed, DeepCABAC is employed for model compression, applying a quantization scheme followed by the context-adaptive binary arithmetic coding (CABAC) entropy coding algorithm, ultimately leading to bitstream generation. Our method outperforms NeRV, as well as x264 and x265, achieving BD-rate gains against NeRV: $-12.06 \%$ using PSNR and $-14.25 \%$ using MS-SSIM. Furthermore, it exhibits superior subjective quality compared to NeRV, attributed to enhanced high-level feature reconstruction. This observed behavior encourages the application of our method to other NeRV-based models, such as E-NeRV.