Improving non-constant luminance color encoding efficiency for high dynamic range video applications

Non-Constant Luminance (NCL) and Constant Luminance (CL) are the two common methods for converting RGB values to luma and chroma for compression efficiency. CL coefficients have been derived from the luminous efficacy of the used gamut color primaries in the light linear domain. NCL applies the same coefficients but on non-linear inputs, which are perceptually encoded values using proper transfer function, thus leading to reduced compression efficiency and color shifts. However, since legacy cameras capture perceptually encoded values of light, it is common practice to use NCL in the existing video distribution pipelines. Although color distortion was not a serious problem with legacy Standard Dynamic Range (SDR) systems, this is not the case with High Dynamic Range (HDR) applications where color shifts become much more visible and prohibitive to delivering high quality HDR. In this thesis, we propose methods that address the inefficiencies of the conventional NCL method by optimizing NCL luma values to be as close as possible to those of CL, thus improving compression performance and color accuracy, while maintaining the current pipeline infrastructure. First, we develop a global optimization method for deriving new optimum coefficients that approximate NCL values to those of the CL approach. Then, we improve upon this approach by conducting content based optimization. This adaptive optimization method takes content pixel density into consideration and optimizes only based on these color distributions. Finally, we propose a weighted global optimization method, which separates chromaticity into three categories (Red, Green, and Blue), and assigns weights based on their contributions to luminance. Evaluations show that the proposed method improves color quality and compression efficiency over NCL.Applied Science, Faculty ofElectrical and Computer Engineering, Department ofGraduat