Non-professional video, commonly known as User Generated Content (UGC) has
become very popular in today's video sharing applications. However, traditional
metrics used in compression and quality assessment, like BD-Rate and PSNR, are
designed for pristine originals. Thus, their accuracy drops significantly when
being applied on non-pristine originals (the majority of UGC). Understanding
difficulties for compression and quality assessment in the scenario of UGC is
important, but there are few public UGC datasets available for research. This
paper introduces a large scale UGC dataset (1500 20 sec video clips) sampled
from millions of YouTube videos. The dataset covers popular categories like
Gaming, Sports, and new features like High Dynamic Range (HDR). Besides a novel
sampling method based on features extracted from encoding, challenges for UGC
compression and quality evaluation are also discussed. Shortcomings of
traditional reference-based metrics on UGC are addressed. We demonstrate a
promising way to evaluate UGC quality by no-reference objective quality
metrics, and evaluate the current dataset with three no-reference metrics
(Noise, Banding, and SLEEQ).

该论文介绍了一个大规模的 UGC 数据集，讨论了在此场景下的压缩和质量评估的挑战并针对 UGC 的缺陷提出了无参考客观质量指标来评估 UGC 质量，通过三种这样的指标 (噪音、细节和颜色斑点，以及感知失真预估) 评估了所提供的 UGC 数据集。

用于视频压缩研究的 YouTube UGC 数据集

YouTube UGC Dataset for Video Compression Research

We present a comprehensive study and evaluation of existing single image
deraining algorithms, using a new large-scale benchmark consisting of both
synthetic and real-world rainy images.This dataset highlights diverse data
sources and image contents, and is divided into three subsets (rain streak,
rain drop, rain and mist), each serving different training or evaluation
purposes. We further provide a rich variety of criteria for dehazing algorithm
evaluation, ranging from full-reference metrics, to no-reference metrics, to
subjective evaluation and the novel task-driven evaluation. Experiments on the
dataset shed light on the comparisons and limitations of state-of-the-art
deraining algorithms, and suggest promising future directions.

本文针对单幅图像去雨算法进行了全面的研究和评估，建立了一个大规模的数据集作为对比基准，其中包括从合成图像到真实世界的雨天图像等多种数据来源和图像内容，并将其分为雨纹、雨滴、雨和雾三个子集，每个子集分别服务于不同的训练或评估目的。我们还提供了多种评估标准，从全参考度量到零参考度量，到主观评估和新颖的任务驱动评估。在数据集上的实验揭示了现有单幅图像去雨算法之间的差异和局限性，并提出了有前途的未来方向。

单张图像去雨：全面基准分析

Single Image Deraining: A Comprehensive Benchmark Analysis

We present a comprehensive study and evaluation of existing single image
dehazing algorithms, using a new large-scale benchmark consisting of both
synthetic and real-world hazy images, called REalistic Single Image DEhazing
(RESIDE). RESIDE highlights diverse data sources and image contents, and is
divided into five subsets, each serving different training or evaluation
purposes. We further provide a rich variety of criteria for dehazing algorithm
evaluation, ranging from full-reference metrics, to no-reference metrics, to
subjective evaluation and the novel task-driven evaluation. Experiments on
RESIDE shed light on the comparisons and limitations of state-of-the-art
dehazing algorithms, and suggest promising future directions.

本文使用 RESIDE 数据集，对现有的单幅图像去雾算法进行了全面的研究和评估，提出了丰富的评价指标从多个角度进行度量，并通过实验比较了现有算法的优缺点，探索了未来的研究方向。