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

TL;DR

VideoSet is a large-scale, publicly available dataset of coded video quality measurements based on JND, encompassing diverse resolutions and encoding parameters, aimed at advancing video coding research.

Contribution

This work introduces the first large-scale JND-based coded video quality dataset, including detailed measurement procedures and data analysis for future research and standardization.

Findings

Collected JND data for 880 video clips across four resolutions.

Analyzed properties of JND data and outlier removal methods.

Discussed implications for video coding research and standardization.

Abstract

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., $1920 \times 1080$, $1280 \times 720$, $960 \times 540$ and $640 \times 360$). 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.