COMPPACT: Compression of Video using Perceptually Optimised Parametric Coding Techniques

COMPPACT：使用感知优化参数编码技术压缩视频

• 批准号: EP/J019291/1
• 负责人: David Bull
• 金额: $ 69.71万
• 依托单位: University of Bristol
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2012
• 资助国家: 英国
• 起止时间: 2012 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FJ019291%2F1
• 关键词: COMPPACT; Compression; Video; using; Perceptually

It is currently a very exciting and challenging time for video compression. The predicted growth in demand for bandwidth, especially for mobile services is driven largely by video applications and is probably greater now than it has ever been. There are four reasons for this: (i) Recently introduced formats such as 3D and multiview, coupled with increasing dynamic range, spatial resolution and framerate, all require increased bit-rate to deliver improved immersion; (ii) Video-based web traffic continues to grow and dominate the internet; (iii) User expectations coninue to drive flexibility and quality, with a move from linear to non-linear delivery; (iv) Finally the emergence of new services, in particular mobile delivery through 4G/LTE to smart phones. While advances in network and physical layer technologies will no doubt contribute to the solution, the role of video compression is also of key importance.This research project is underpinned by the assumption that, in most cases, the target of video compression is to provide good subjective quality rather than to minimise the error between the original and coded pictures. It is thus possible to conceive of a compression scheme where an analysis/synthesis framework replaces the conventional energy minimisation approach. Such a scheme could offer substantially lower bitrates through reduced residual and motion vector coding. The approach proposed will model scene content using combinations of waveform coding and texture replacement, using computer graphic models to replace target textures at the decoder. These not only offer the potential for dramatic improvements in performance, but they also provide an inherent content-related parameterisation which will be of use in classification and detection tasks as well as facilitating integration with CGI. This has the potential to create a new content-driven framework for video compression. In this context our aim is to shift the video coding paradigm from rate-distortion optimisation to rate-quality modelling, where region-based parameters are combined with perceptual quality metrics to inform and drive the coding and synthesis processes. However it is clear that a huge amount of research needs to be done in order to fully exploit the method's potential and to yield stable and efficient solutions. For example, mean square error is no longer a valid objective function or measure of quality, and new embedded perceptually driven quality metrics are essential. The choice of texture analysis and synthesis models are also important, as is the exploitation of long-term picture dependencies.

对于视频压缩来说，这是一个非常令人兴奋和具有挑战性的时代。对带宽需求的预期增长，尤其是对移动服务的需求增长，主要是由视频应用程序推动的，现在的增长可能比以往任何时候都要大。这有四个原因：(I)最近推出的3D和多视点格式，加上不断增加的动态范围、空间分辨率和帧速率，都需要更高的比特率来提供更好的沉浸感；(Ii)基于视频的网络流量继续增长并主导互联网；(Iii)用户期望继续提高灵活性和质量，从线性交付转向非线性交付；(Iv)最后出现新服务，特别是通过4G/LTE向智能手机进行移动交付。虽然网络和物理层技术的进步无疑将有助于解决这一问题，但视频压缩的作用也是关键的。本研究项目的基础假设是，在大多数情况下，视频压缩的目标是提供良好的主观质量，而不是将原始图像和编码图像之间的误差降至最低。因此，可以设想一种压缩方案，其中分析/合成框架取代传统的能量最小化方法。这样的方案可以通过减少残差和运动矢量编码来提供显著更低的比特率。所提出的方法将使用波形编码和纹理替换的组合来对场景内容进行建模，使用计算机图形模型来替换解码器中的目标纹理。这些不仅提供了显著提高性能的潜力，而且还提供了与内容相关的固有参数，这将在分类和检测任务中使用，并促进与CGI的集成。这有可能创建一种新的内容驱动的视频压缩框架。在这种情况下，我们的目标是将视频编码范例从率失真优化转变为率质量建模，其中基于区域的参数与感知质量度量相结合来通知和驱动编码和合成过程。然而，显然需要进行大量的研究，以便充分开发该方法的潜力，并产生稳定和有效的解决办法。例如，均方误差不再是有效的目标函数或质量度量，而新的嵌入感知驱动的质量度量是必不可少的。纹理分析和合成模型的选择也很重要，开发长期图片依赖关系也很重要。

项目成果

BVI-HD: A Video Quality Database for HEVC Compressed and Texture Synthesized Content

• DOI: 10.1109/tmm.2018.2817070
• 发表时间: 2018-03
• 期刊: IEEE Transactions on Multimedia
• 影响因子: 7.3
• 作者: Fan Zhang;Felix J. Mercer Moss;R. Baddeley;D. Bull

Rate-Distortion Optimization Using Adaptive Lagrange Multipliers

• DOI: 10.1109/tcsvt.2018.2873837
• 发表时间: 2019-10
• 期刊: IEEE Transactions on Circuits and Systems for Video Technology
• 影响因子: 8.4
• 作者: Fan Zhang;D. Bull

A Perception-Based Hybrid Model for Video Quality Assessment

• DOI: 10.1109/tcsvt.2015.2428551
• 发表时间: 2016-06
• 期刊: IEEE Transactions on Circuits and Systems for Video Technology
• 影响因子: 8.4
• 作者: Fan Zhang;D. Bull

On the Optimal Presentation Duration for Subjective Video Quality Assessment

• DOI: 10.1109/tcsvt.2015.2461971
• 发表时间: 2016-11
• 期刊: IEEE Transactions on Circuits and Systems for Video Technology
• 影响因子: 8.4
• 作者: Felix J. Mercer Moss;Ke Wang;Fan Zhang;R. Baddeley;D. Bull

Quality assessment methods for perceptual video compression

• DOI: 10.1109/icip.2013.6738009
• 发表时间: 2013-09
• 期刊: 2013 IEEE International Conference on Image Processing
• 作者: Fan Zhang;D. Bull