RI: Small: Intelligent Autonomous Video Quality Agents

确定通过复杂网络传输并在从手机到基于互联网的电视等异构平台上观看的视频的感知质量，是YouTube一代面临的关键问题。它也是各种视觉应用的核心，包括人脸检测、人脸识别和监视。视频受到许多失真：模糊、噪声、压缩、数据包/帧丢失等。当无法获得未失真的视频时，质量评估是非常重要的，并且对于多种失真类型和分布的非平稳观看环境无法解决。本项目设计并创建智能视频“质量代理”，学习如何在异构网络中确定感知视频质量，并评估其对人脸检测和识别等决策任务的影响，所有这些都没有参考视频的好处。它利用自然场景的统计特性、感知原理、机器学习和智能自适应代理集体来处理同时受到多种失真类型损害的视频。一个主要的应用是新的人脸显著性质量评估代理和质量感知人脸检测算法。在移动设备上使用基于主动学习的反馈机制训练多个协作视频和人脸质量代理。该项目在现实生活网络中产生自适应、鲁棒的视频服务质量评估，并为人类视觉质量感知和视觉失真检测提供了新的见解。研究小组还创建了两个大型、独特的视频质量数据库：(a)包含原始和失真移动视频的移动视频质量数据库和(b)包含未失真和失真人脸图像的失真人脸数据库，作为这一领域研究和发展的黄金标准。

Determining the perceptual quality of video transmitted through complex networks and viewed on heterogeneous platforms, from cell phones to Internet-based television, is a key problem for the YouTube generation. It is also central to a variety of vision applications including face detection, face recognition and surveillance. Video is subject to numerous distortions: blur, noise, compression, packet/frame drops, etc. Quality assessment is non-trivial when an undistorted video is not available, and unsolved for multiple distortion types and in distributed, non-stationary viewing environments.This project designs and creates intelligent video "quality agents" that learn how to determine perceptual video quality in heterogeneous networks, and assesses its impact on decision tasks such as face detection and recognition, all without the benefit of reference videos. It uses statistical properties of natural scenes, perceptual principles, machine learning, and intelligent adaptive agent collectives to handle videos simultaneously impaired by multiple distortion types. A primary application is novel face-salient quality assessment agents and quality-aware face detection algorithms. Multiple, co-operative video and face quality agents are trained using active learning based feedback mechanisms on mobile devices. This project yields adaptive, robust video Quality of Service assessment in real-life networks and provides new insights into human visual quality perception and visual distortion detection. The research team also creates two large, unique video quality databases: (a) A Mobile Video Quality Database of raw and distorted mobile videos and (b) A Distorted Face Database of undistorted and distorted face images, as gold standards for research and development in this area.