# ckkelvinchan/RealBasicVSR

Official repository of "Investigating Tradeoffs in Real-World Video Super-Resolution"

Repository: https://github.com/ckkelvinchan/RealBasicVSR
Canonical: https://ross.abutalabs.com/products/realbasicvsr
Language: Python
License: Apache-2.0
License Family: permissive
Last push: 2023-06-05T13:22:04+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 1756, "days_push": 1185, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1100, forks 150 (observed 2026-08-28T04:03:35.501046+00:00)

## What it is
Official PyTorch implementation of RealBasicVSR, a real-world video super-resolution model from CVPR 2022, built on MMEditing. It provides training/inference code, pre-trained weights, a Colab demo, and the VideoLQ benchmark dataset.

## Use cases
- upscale low-quality real-world videos
- video super-resolution with pretrained model
- denoise and enhance compressed video footage
- benchmark video restoration on VideoLQ dataset
- train a video super-resolution model
- restore old or blurry video clips

## When to choose
- you need state-of-the-art real-world video super-resolution with pretrained weights
- you want a research-grade codebase built on MMEditing for video restoration
- you need the VideoLQ benchmark for evaluating video restoration models

## When to avoid
- you need real-time or low-latency upscaling - the model is computationally heavy
- you want a maintained production library - development has largely moved to MMEditing
- you only need simple image (not video) super-resolution

## Facets
- artifact type: library
- maturity: maintenance
- function: video-processing, image-processing, machine-learning, deep-learning
- domain: computer-vision, image-processing, deep-learning
- platform: python
- tags: super-resolution, video-super-resolution, cvpr-2022, pytorch, mmediting, research-code, pretrained-models, video, gpu, linux

## Member repositories
- ckkelvinchan/RealBasicVSR (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:03:35.501046+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T06:45:47.449076+00:00, confidence not recorded.
  - readme: https://github.com/ckkelvinchan/RealBasicVSR (fetched 2026-08-28T04:03:35.501046+00:00, sha 6c931310eb8e)
- Data as of 2026-08-30T08:39:29.467469+00:00.