# Neural video codecs quality assessment dataset and benchmark

arXiv: https://arxiv.org/abs/2608.29331 · HTML: https://mlsanity.com/paper/2608.29331/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 49 references that are in the map.
- First posted on arXiv 29 Aug 2026.

## Builds on (most cited first, 16 of 16)

- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [Exploring CLIP for Assessing the Look and Feel of Images](https://mlsanity.com/paper/2207.12396/) (2022)
- [Image Quality Assessment: Unifying Structure and Texture Similarity](https://mlsanity.com/paper/2004.07728/) (2020)
- [DVC: An End-to-end Deep Video Compression Framework](https://mlsanity.com/paper/1812.00101/) (2018)
- [Blind Image Quality Assessment Using A Deep Bilinear Convolutional Neural Network](https://mlsanity.com/paper/1907.02665/) (2019)
- [RankIQA: Learning from Rankings for No-reference Image Quality Assessment](https://mlsanity.com/paper/1707.08347/) (2017)
- [Patch-VQ: 'Patching Up' the Video Quality Problem](https://mlsanity.com/paper/2011.13544/) (2020)
- [MetaIQA: Deep Meta-learning for No-Reference Image Quality Assessment](https://mlsanity.com/paper/2004.05508/) (2020)
- [Unified Quality Assessment of In-the-Wild Videos with Mixed Datasets Training](https://mlsanity.com/paper/2011.04263/) (2020)
- [KVQ: Kwai Video Quality Assessment for Short-form Videos](https://mlsanity.com/paper/2402.07220/) (2024)
- [VideoSet: A Large-Scale Compressed Video Quality Dataset Based on JND Measurement](https://mlsanity.com/paper/1701.01500/) (2017)
- [Measuring the Quality of Text-to-Video Model Outputs: Metrics and Dataset](https://mlsanity.com/paper/2309.08009/) (2023)
- [Subjective Assessment of High Dynamic Range Videos Under Different Ambient Conditions](https://mlsanity.com/paper/2209.10005/) (2022)
- [EHVC: Efficient Hierarchical Reference and Quality Structure for Neural Video Coding](https://mlsanity.com/paper/2509.04118/) (2025)
- [Neural B-frame Video Compression with Bi-directional Reference Harmonization](https://mlsanity.com/paper/2511.08938/) (2025)
- [Evaluating Video Quality Metrics for Neural and Traditional Codecs using 4K/UHD-1 Videos](https://mlsanity.com/paper/2511.00969/) (2025)

## Abstract (arXiv)

Video traffic constitutes a significant share of global web traffic. To reduce its volume, video codecs have been developed and continuously improved. While the industry has achieved substantial progress in traditional video coding, neural video codecs (NVCs) have recently emerged as a new approach that applies deep learning to video compression. This creates new challenges for compression quality assessment, which is essential for the further development and improvement of such codecs. In particular, it is important to evaluate the novel temporal compression paradigms introduced by NVCs. In this work, we present a large-scale subjective dataset of videos compressed with both neural and traditional video codecs. The subjective scores were collected through crowd-sourced pairwise comparisons. The proposed dataset provides a valuable resource for the development and benchmarking of video quality metrics tailored to neural video codecs. The dataset is available at the following link: https://videoprocessing.github.io/nvc-dataset-benchmark