Joint Fidelity and Perceptual Modulation via Adaptive Corrections for Implicit-Motion Neural Video Compression

Implicit-motion neural video codecs achieve remarkable throughput by modeling motion entirely within high-dimensional contextual features. However, this highly coupled temporal representation causes quantization errors to propagate implicitly, limiting both rate-distortion and perceptual performance at high bitrates. To bridge this gap without compromising decoding efficiency, we propose a temporally consistent and perceptual-aware encoder-side latent refinement framework. Our method operates exclusively during encoding, ensuring that the decoder side and bitstream format remain entirely unchanged. First, we introduce a joint optimization strategy across I-frame and P-frame chunks that balances fidelity and perceptual objectives. The optimization leverages a simulated gradient annealing process, augmented by a residual direction search, to ensure robust convergence within the discrete latent space. Second, we explicitly model the future-reference dependency inherent in implicit codecs through a lookahead mechanism. By combining the lookahead reconstruction of future frames with cycle-consistent bidirectional optical flow, we construct a temporally corrected Region-of-Interest (ROI). Guided by this ROI, the optimization explicitly directs bit allocation toward areas that serve as critical temporal references along reliable motion trajectories. Extensive experiments demonstrate the superiority of our approach. On HEVC Class C and D sequences, compared to the frozen DCVC-UF (HT-L) baseline, our method achieves average BD-Rate reductions of 13.27% (PSNR), 18.06% (LPIPS), and 28.94% (DISTS) over all frames, confirming that targeted test-time optimization effectively eliminates the high-rate bottleneck of implicit-motion architectures.