视频大数据研究综述

A Review on Video Big Data

科学技术与生产力的发展带来了数据量的高速增长,其中视频图像等多媒体数据占了很大的比重。如何高效处理这些海量数据并从中快速挖掘有价值的信息是当前的研究热点。通常大数据具有四个特点,即数据量大、需要快速响应、数据类型多样和价值密度低。视频大数据同样具有以上特点,但其特殊性在于数据冗余更大,需要进行高效的压缩编码与分析处理。总的来说,视频大数据的研究内容包括了视频数据表示、智能视频分析、视频压缩与传输、视频显示与评价等方面。在发展趋势上,视频数据的表示将向真实感与智能化两个方向发展;智能视频分析技术将会借助深度神经网络获得更准确的识别分类结果;视频压缩技术在提升压缩效率的同时也会探索降低编码复杂度的方法,并通过结合人眼视觉感知特性的编码算法来减少视频大数据的视觉冗余;视频显示设备将伴随着视频数据表示形式的改变而进行相应的升级换代;视频质量的评价准则将由单一的图像质量评价向更加综合全面的用户体验质量评价发展。

The developments of science and technology have brought rapid growth of data, of which video and image data account for a high percentage. How to efficiently handle these data and find valuable information from them is a hot topic. Big data are characterized by four Vs： volume, velocity, variety, and value, representing large amounts of data, quick data processing, various data types, and low value density, respectively. Video big data share all these characteristics, and often come with much greater data redundancy than other types of data. As a result, they call for more efficient techniques for compression and processing. The research of video big data is primarily carried out along four dimensions： video data representation, intelligent video analysis, video compression and transmission, and video display and quality evaluation. Recent trends show that video representation is becoming more realistic and intelligent, and video analysis more accurate in identification and classification thanks to the deep neural networks. At the same time, video compression promises to be more efficient with new methods to reduce coding complexity, and less redundant with the help of visual perception aware coding algorithms. In accordance with more advanced video representation, video display devices are undergoing hardware upgrades, guided by a comprehensive methodology of video quality evaluation that is centered around quality of experience, instead of the traditional criteria developed for image quality assessment.