High dynamic range (HDR) imaging techniques enable the full range of light in a scene to be captured, transmitted and displayed, eliminating under- and over-exposed regions. Storing the full range of light typically requires 32 bits of information per colour channel, four times larger than the 8 bits required for low dynamic range (LDR) data. If HDR is to fulfil its potential in a wide variety of applications such as live broadcast and interactive remote gaming, fast, efficient compression is required to provide HDR video without major changes to existing communications infrastructure. A number of methods have so far been proposed for HDR video compression, however they rely on computationally expensive transformations to either split the video into multiple 8-bit streams or convert to a perceptually uniform domain. This thesis will address the question of whether high-quality HDR video compression can be achieved in a computationally efficient manner by introducing a number of novel techniques. Initially, the power-law functions used by LDR video are extended to HDR data to provide a straightforward and efficient solution to HDR compression. The Power Transfer Function (PTF) is computationally inexpensive and an objective evaluation shows that it provides improved compression quality at a thirtieth of the computational cost of other leading methods while maintaining equivalent perceived quality. Subsequently, information about the content and ambient environment at the display are used to adaptively transform the compression method. A subjective evaluation involving 40 participants demonstrates that the necessary peak luminance of content is dependent on the ambient illumination of the display, and an objective evaluation confirms that optimal compression is affected by the peak luminance. An adaptive extension to PTF, Adaptive PTF (PTFa) is proposed using iterative optimisation to calculate the ideal compression curve, gaining 2.88 VDP over non-adaptive PTF. Finally, the computational performance of PTFa is improved by four orders of magnitude to enable real-time compression with little decrease in quality through deep learning. Predictive PTF (PTFp) utilises a model to predict the compression curved based on the content, display and ambient environment. This thesis demonstrates a fast, efficient method of general HDR video compression which is extended to provide a fast, adaptive solution for content-specific compression.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:752471 |
| Date | January 2017 |
| Creators | Hatchett, Jonathan |
| Publisher | University of Warwick |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | http://wrap.warwick.ac.uk/106777/ |
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