BVI-DVC

Deep learning methods are increasingly being applied in the optimisation of video compression algorithms and can achieve significantly enhanced coding gains, compared to conventional approaches. Such approaches often employ Convolutional Neural Networks (CNNs) which are trained on databases with relatively limited content coverage. BVI-DVC is a new extensive and representative video database for training CNN-based coding tools, which contains 800 sequences at various spatial resolutions from 270p to 2160p. Experimental results show that the database produces significant improvements in terms of coding gains over three existing (commonly used) image/video training databases.

深度学习方法正日益广泛地应用于视频压缩算法的优化工作中，相较于传统方法，其可实现显著提升的编码增益。此类方法通常采用卷积神经网络（Convolutional Neural Networks, CNNs），这类网络的训练数据库内容覆盖范围相对有限。BVI-DVC是一款全新、覆盖范围广泛且具有代表性的视频数据库，用于训练基于卷积神经网络的编码工具，该数据库包含800段不同空间分辨率的视频序列，分辨率覆盖范围从270p至2160p。实验结果表明，相较于三款现有（常用）图像/视频训练数据库，该数据库可在编码增益方面取得显著提升。

搜集汇总

背景与挑战

背景概述

BVI-DVC是一个包含800个不同分辨率视频序列的数据库，专为训练基于CNN的视频压缩工具设计，能显著提升编码效率。由于版权限制，数据集不公开，但提供了开放的子集BVI-DVC Part 1。