This paper reports on the NTIRE 2026 Challenge on Bitstream-Corrupted Video Restoration (BSCVR). The challenge aims to advance research on recovering visually coherent videos from corrupted bitstreams, whose decoding often produces severe spatial-temporal artifacts and content distortion. Built upon recent progress in bitstream-corrupted video recovery, the challenge provides a common benchmark for evaluating restoration methods under realistic corruption settings. We describe the dataset, evaluation protocol, and participating methods, and summarize the final results and main technical trends. The challenge highlights the difficulty of this emerging task and provides useful insights for future research on robust video restoration under practical bitstream corruption.
Figures & tables
Figure 1: Video corruption pattern in bitstream-corrupted video recovery problem summarized by [ 33 ] . Compared with the simulated video corruption in existing inpainting or error concealment research, BSCVR contains various realistic corruption patterns including (1) block artifacts (artfs.), (2) color artifacts, (3) duplication artifacts, (4) misalignment, (5) texture loss, (6) trailing artifacts, which is closer to the corrupted video in real world.
NTIRE 2026 Challenge on Bitstream-Corrupted Video Restoration Results
Team
Codabench User
PSNR ↑ (Primary)
SSIM ↑
LPIPS ↓
Rank PSNR
Rank SSIM
Final Rank
MGTV-AI
nerror
33.642
0.9334
0.0900
1
2
1
RedMediaTech
chenyuxiang
32.865
0.9344
0.0852
2
1
2
bighit
hyena
27.873
0.8933
0.1388
3
3
3
Vroom
priyansh
27.370
0.8713
0.2028
4
5
4
weichow
weichow
27.276
0.8727
0.1866
5
4
5
Table 1: NTIRE 2026 Bitstream-Corrupted Video Restoration Challenge results, final rankings, and the main characteristics of the solutions. Note that, the average PSNR value achieved on the test set is used for final ranking.
Figure 2: Visualization of the final results of participants, along with corresponding input reference frames and GT images. Each column represents a video of a different scene. Among them, (a-i) respectively represent the results of Bitstream Corrupted Input, GT, MGTV-AI team, RedMediaTech team, Bighit team, Vroom team, Weichow team, Holding team, and NTR team.
Figure 3: The MGTV-AI Team: Three-Stage Framework For BitStream-corrupted Video Restoration.
Figure 4: The RedMediaTech team proposed a single-step video restoration framework based on Wan2.1 DiT with a two-stage training strategy. The first stage leverages MSE and LPIPS losses to exploit strong generative priors for perceptual quality, while the second stage fine-tunes with MSE loss to improve distortion metrics (PSNR/SSIM). Additionally, replacing the original VAE with Qwen-Image VAE enhances robustness to large motion and complex temporal variations.
Figure 5: The bighit team proposed two-stage framework for bitstream-corrupted video restoration. Corrupted frame sequences and masks are first fused with SAM2 structural embeddings and DINOv3 semantic tokens. A semantic-memory retrieval branch and an router-guided MoE-LoRA adaptation branch are jointly used for stage-1 content recovery. An optional stage-2 refinement module further suppresses residual artifacts and improves boundary consistency to produce the final recovered frame sequence.
Figure 6: The Vroom team: Enhanced B2SCVR: SAM2-Prior Guided Bitstream-Corrupted Video Restoration with LoRA and Boundary Refinement.
Figure 7: Overview of weichow team pipeline. The B2SCVR model processes frames at 432 × 240 (its native training resolution). Restored corrupted regions are upscaled to 1280 × 720 and composited with original uncorrupted pixels via binary masks. Non-corrupted frames bypass the model entirely for zero quality loss.
Figure 8: The holding team: Beyond Missing Holes: Taming Feature Leakage in Mask-Guided Bitstream-Corrupted Video Recovery. Built on a bidirectional propagation and temporal-transformer restoration backbone, our framework decomposes the input into valid and corrupted streams, suppresses unreliable corrupted-but-visible residuals with M1, retrieves cross-frame evidence through the target-centric cross-frame attention module (CA), and refines boundary seams with M2 after coarse restoration.