Performance Bounds on Image and Video Compression

目前多媒体应用和服务的爆炸式增长主要是由于图像和视频压缩技术最近的重大进步，例如，用于静止图像的JPEG和用于视频的MPEG，它们已经成为家喻户晓和营销的术语。数字高清晰度电视即将问世，它将基于MPEG2标准。这是否意味着图像/视频编码已经达到饱和状态，对压缩的更多研究不太可能产生重大改进？这项研究试图通过调查图像和视频压缩的基本性能界限来阐明这一点。一个主要目标是发现现有商业系统与最佳理论性能之间的性能差距，并指导未来改进的压缩算法的生成。这项研究的主要焦点是推导出现实类图像和视频模型的基本率-失真界限。这使得能够生成对这些模型理论上可获得的最佳性能与基于它们的特定编码算法之间的差距的可靠估计。该研究的分析方法包括以下几个关键部分：(1)日益复杂和逼真的图像/视频模型的层次结构；(2)这些模型的率失真界限的推导；(3)模型参数可辨识性和估计精度的分析；(4)基于通用频谱盲空时采样技术的压缩；(5)基于高性能实用算法的真实图像/视频的模型验证。

The current explosion in multimedia applications and services has been primarily enabled by the significant recent advances in image and video compression technology, e.g., JPEG for still images and MPEG for video, which have become household and marketing terms. Digital HDTV is on the horizon, and will be based on the MPEG-2 standard. Does this mean that image/video coding has reached a state of saturation where more research on compression is unlikely to yield significant improvements? This research tries to shed light on this by investigating the fundamental performance bounds on image and video compression. A primary goal is to uncover the performance gaps between existing commercial systems and the theoretical performance optimally attainable, and to guide the generation of improved compression algorithms in the future. The main focus of this research is to derive fundamental rate--distortion bounds for realistic classes of image and video models. This enables the generation of reliable estimates of the gap between the optimal performance theoretically attainable for these models and that of specific coding algorithms based on them. The analytical approach of this research contains the following key components: (1) a hierarchy of increasingly complex and realistic image/video models; (2) derivation of rate-distortion bounds for these models; (3) analysis of model parameter identifiability and estimation accuracy; (4) compression based on universal spectrum-blind spatio-temporal sampling techniques, and (5) model validation on real images/video based on high-performance practical algorithms.