BVI-DVC Part 1

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 772 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应用于视频压缩算法的优化工作，相较传统方案，可实现显著更优的编码增益。此类方法通常采用卷积神经网络（Convolutional Neural Networks, CNNs），这类网络会在内容覆盖范围相对有限的数据集上进行训练。BVI-DVC是一款全新且覆盖范围广泛、极具代表性的视频数据集，用于训练基于卷积神经网络的编码工具，该数据集包含772段空间分辨率覆盖270p至2160p的视频序列。实验结果表明，相较于三款现有（通用）图像/视频训练数据集，该数据集可在编码增益方面带来显著提升。

搜集汇总

背景与挑战

背景概述

BVI-DVC Part 1是一个用于训练基于CNN的视频压缩算法的综合性视频数据库，包含772个分辨率从270p到2160p不等的视频序列，相比现有训练数据库能显著提高编码性能。