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Characterizing Generalized Rate-Distortion Performance of Video Coding: An Eigen Analysis Approach
arXiv - CS - Multimedia Pub Date : 2019-12-15 , DOI: arxiv-1912.07126
Zhengfang Duanmu (1), Wentao Liu (1), Zhuoran Li (1), Kede Ma (2) and Zhou Wang (1) ((1) University of Waterloo, Canada, (2) City University of Hong Kong, Hong Kong, China)

Rate-distortion (RD) theory is at the heart of lossy data compression. Here we aim to model the generalized RD (GRD) trade-off between the visual quality of a compressed video and its encoding profiles (e.g., bitrate and spatial resolution). We first define the theoretical functional space $\mathcal{W}$ of the GRD function by analyzing its mathematical properties.We show that $\mathcal{W}$ is a convex set in a Hilbert space, inspiring a computational model of the GRD function, and a method of estimating model parameters from sparse measurements. To demonstrate the feasibility of our idea, we collect a large-scale database of real-world GRD functions, which turn out to live in a low-dimensional subspace of $\mathcal{W}$. Combining the GRD reconstruction framework and the learned low-dimensional space, we create a low-parameter eigen GRD method to accurately estimate the GRD function of a source video content from only a few queries. Experimental results on the database show that the learned GRD method significantly outperforms state-of-the-art empirical RD estimation methods both in accuracy and efficiency. Last, we demonstrate the promise of the proposed model in video codec comparison.

中文翻译:

表征视频编码的广义速率失真性能:一种特征分析方法

率失真 (RD) 理论是有损数据压缩的核心。在这里,我们旨在对压缩视频的视觉质量与其编码配置文件(例如,比特率和空间分辨率)之间的广义 RD (GRD) 权衡进行建模。我们首先通过分析其数学性质定义了 GRD 函数的理论函数空间 $\mathcal{W}$。 我们证明 $\mathcal{W}$ 是 Hilbert 空间中的凸集,启发了 GRD 的计算模型函数,以及一种从稀疏测量中估计模型参数的方法。为了证明我们想法的可行性,我们收集了真实世界 GRD 函数的大规模数据库,结果证明这些函数存在于 $\mathcal{W}$ 的低维子空间中。结合GRD重建框架和学习到的低维空间,我们创建了一种低参数特征 GRD 方法,以仅从几个查询中准确估计源视频内容的 GRD 函数。数据库上的实验结果表明,学习到的 GRD 方法在准确性和效率方面都明显优于最先进的经验 RD 估计方法。最后,我们证明了所提出模型在视频编解码器比较中的前景。
更新日期:2020-06-24
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