# VideoSet: A Large-Scale Compressed Video Quality Dataset Based on JND Measurement

arXiv: https://arxiv.org/abs/1701.01500 · HTML: https://mlsanity.com/paper/1701.01500/

## Key facts

- Cited by 33 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Jan 2017.

## Built on by (most cited first, 30 of 33)

- [MD-VQA: Multi-Dimensional Quality Assessment for UGC Live Videos](https://mlsanity.com/paper/2303.14933/) (2023)
- [Perceptual Attacks of No-Reference Image Quality Models with Human-in-the-Loop](https://mlsanity.com/paper/2210.00933/) (2022)
- [Video compression dataset and benchmark of learning-based video-quality metrics](https://mlsanity.com/paper/2211.12109/) (2022)
- [Just Noticeable Difference for Deep Machine Vision](https://mlsanity.com/paper/2102.08168/) (2021)
- [Subjective and Objective Analysis of Streamed Gaming Videos](https://mlsanity.com/paper/2203.12824/) (2022)
- [Perceptual Quality Assessment of Face Video Compression: A Benchmark and An Effective Method](https://mlsanity.com/paper/2304.07056/) (2023)
- [Subjective Image Quality Assessment with Boosted Triplet Comparisons](https://mlsanity.com/paper/2108.00201/) (2021)
- [Just Noticeable Difference for Large Multimodal Models](https://mlsanity.com/paper/2507.00490/) (2025)
- [VADB: A Large-Scale Video Aesthetic Database with Professional and Multi-Dimensional Annotations](https://mlsanity.com/paper/2510.25238/) (2025)
- [Perceptual Video Coding for Machines via Satisfied Machine Ratio Modeling](https://mlsanity.com/paper/2211.06797/) (2022)
- [AIM 2024 Challenge on Compressed Video Quality Assessment: Methods and Results](https://mlsanity.com/paper/2408.11982/) (2024)
- [BAND-2k: Banding Artifact Noticeable Database for Banding Detection and Quality Assessment](https://mlsanity.com/paper/2311.17752/) (2023)
- [Full RGB Just Noticeable Difference (JND) Modelling](https://mlsanity.com/paper/2203.00629/) (2022)
- [Convex Hull Prediction for Adaptive Video Streaming by Recurrent Learning](https://mlsanity.com/paper/2206.04877/) (2022)
- [Telepresence Video Quality Assessment](https://mlsanity.com/paper/2207.09956/) (2022)
- [SG-JND: Semantic-Guided Just Noticeable Distortion Predictor For Image Compression](https://mlsanity.com/paper/2408.04273/) (2024)
- [Deep Quality Assessment of Compressed Videos: A Subjective and Objective Study](https://mlsanity.com/paper/2205.03630/) (2022)
- [DCVQE: A Hierarchical Transformer for Video Quality Assessment](https://mlsanity.com/paper/2210.04377/) (2022)
- [Localization of Just Noticeable Difference for Image Compression](https://mlsanity.com/paper/2306.07678/) (2023)
- [Fine-grained subjective visual quality assessment for high-fidelity compressed images](https://mlsanity.com/paper/2410.09501/) (2024)
- [DT-JRD: Deep Transformer based Just Recognizable Difference Prediction Model for Video Coding for Machines](https://mlsanity.com/paper/2411.09308/) (2024)
- [HVS-Inspired Signal Degradation Network for Just Noticeable Difference Estimation](https://mlsanity.com/paper/2208.07583/) (2022)
- [IQNet: Image Quality Assessment Guided Just Noticeable Difference Prefiltering For Versatile Video Coding](https://mlsanity.com/paper/2312.09799/) (2023)
- [Perceptual Learned Image Compression via End-to-End JND-Based Optimization](https://mlsanity.com/paper/2402.02836/) (2024)
- [Predicting Satisfied User and Machine Ratio for Compressed Images: A Unified Approach](https://mlsanity.com/paper/2412.17477/) (2024)
- [LEHA-CVQAD: Dataset To Enable Generalized Video Quality Assessment of Compression Artifacts](https://mlsanity.com/paper/2507.03990/) (2025)
- [Super-Resolving Compressed Video in Coding Chain](https://mlsanity.com/paper/2103.14247/) (2021)
- [Subjective and Objective Quality Assessment of Banding Artifacts on Compressed Videos](https://mlsanity.com/paper/2508.08700/) (2025)
- [Analytic Score Optimization for Multi Dimension Video Quality Assessment](https://mlsanity.com/paper/2602.16856/) (2026)
- [Multi-task Just Recognizable Difference for Video Coding for Machines: Database, Model, and Coding Application](https://mlsanity.com/paper/2604.09421/) (2026)

## Abstract (arXiv)

A new methodology to measure coded image/video quality using the just-noticeable-difference (JND) idea was proposed. Several small JND-based image/video quality datasets were released by the Media Communications Lab at the University of Southern California. In this work, we present an effort to build a large-scale JND-based coded video quality dataset. The dataset consists of 220 5-second sequences in four resolutions (i.e., 1920 × 1080, 1280 × 720, 960 × 540 and 640 × 360). For each of the 880 video clips, we encode it using the H.264 codec with QP=1, cdots, 51 and measure the first three JND points with 30+ subjects. The dataset is called the "VideoSet", which is an acronym for "Video Subject Evaluation Test (SET)". This work describes the subjective test procedure, detection and removal of outlying measured data, and the properties of collected JND data. Finally, the significance and implications of the VideoSet to future video coding research and standardization efforts are pointed out. All source/coded video clips as well as measured JND data included in the VideoSet are available to the public in the IEEE DataPort.