Rfid Rfvd Psnr

rfid-rfvd-psnr

Generative Refinement Networks for Visual Synthesis — Jian Han et al. (arXiv:2604.13030, 2026)

What this evaluates

Measures the reconstruction fidelity of the visual tokenizer by comparing discrete latent reconstructions to original images/videos.

Datasets

• ImageNet — total ?; splits: test (50000)

Metrics

• rFID (primary) — range: other
  • Reconstruction Fréchet Inception Distance. Lower indicates better perceptual fidelity of reconstructed images.

Input / output format

Input: Original image

Output: Reconstructed image from discrete tokens

Scoring recipe

recon_images = []
for img in test_set:
    tokens = tokenizer.encode(img)
    recon = tokenizer.decode(tokens)
    recon_images.append(recon)
rfid = compute_fid(imgs, recon_images)

Common pitfalls

• rFID is computed on reconstructed images, not generated ones.
• Metrics like PSNR and SSIM measure pixel-level fidelity, while rFID/LPIPS measure perceptual quality.

Evidence (verbatim from paper)

our tokenizer demonstrates state-of-the-art reconstruction performance on the 256x256 ImageNet benchmark. Utilizing four HBQ rounds, it achieves a remarkable rFID of 0.56.

Citation

@misc{han2026generativerefinement,
  title={Generative Refinement Networks for Visual Synthesis},
  author={Jian Han et al.},
  year={2026},
  note={arXiv:2604.13030}
}

• arXiv: 2604.13030