In my current postdoctoral and previous PhD research undertakings, there is a focused concentration on the development of perceptually optimised video compression algorithms (i.e., visually lossless coding) for the HEVC standard. HEVC (ISO/IEC 23008–2, ITU-T H.265) is a video compression platform that has been internationally standardised by JCT-VC (VCEG of ITU-T and also MPEG of ISO and IEC). With application to various types of YCbCr and RGB data, I am presently developing visually lossless compression mathematical models and algorithms, for which I utilise both MATLAB and C++. The raw YCbCr and RGB video data to which the proposed algorithms can be applied includes medical image data and various types of screen content including camera-captured content and animated content. My postdoctoral work follows on from the research that I conducted during the PhD degree in Computer Science.
Working Papers in Progress (Postdoctoral Research)
Lee Prangnell and Victor Sanchez, "JNCD-Based Visually Lossless Compression of RGB Data", IEEE Data Compression Conference, Snowbird, Utah, USA, 2020. (Abstract).
Lee Prangnell and Victor Sanchez, "Spatiotemporal Adaptive Quantization for Video Compression Applications", IEEE International Conference on Acoustics, Speech and Signal Processing, Barcelona, Spain, 2020. (Abstract).
Lee Prangnell, "The Overlooked Importance of HVS Spectral Sensitivity and Full Colour Perception in Visually Lossless Coding: A Conceptual Overview", Elsevier Information Sciences Journal, 2019. (Abstract).
Lee Prangnell and Victor Sanchez, "Minimizing Compression Artifacts for High Resolutions with Adaptive Quantization Matrices for HEVC", arXiv, 2019. (Preprint PDF).
Lee Prangnell, "Frequency-Dependent Perceptual Quantisation for Visually Lossless Compression Applications", arXiv, 2019. (Preprint PDF).
Lee Prangnell, "Visible Light-Based Human Visual System Conceptual Model", arXiv, 2019. (Preprint PDF).
Lee Prangnell, "Visually Lossless Coding in HEVC: A High Bit Depth and 4:4:4 Capable JND-Based Perceptual Quantisation Technique for HEVC", Elsevier Signal Processing: Image Communication Journal, April 2018, DOI: 10.1016/j.image.2018.02.007. (PDF).
Lee Prangnell and Victor Sanchez, "JND-Based Perceptual Video Coding for 4:4:4 Screen Content Data in HEVC", IEEE International Conference on Acoustics, Speech and Signal Processing, Calgary, Alberta, Canada, 2018, DOI: 10.1109/ICASSP.2018.8462327. (PDF).
Lee Prangnell, Miguel Hernández-Cabronero and Victor Sanchez, "Coding Block-Level Perceptual Video Coding for 4:4:4 Data in HEVC", IEEE International Conference on Image Processing, Beijing, China, 2017, DOI: 10.1109/ICIP.2017.8296730. (PDF).
Lee Prangnell, Miguel Hernández-Cabronero and Victor Sanchez, "Cross-Color Channel Perceptually Adaptive Quantization for HEVC", IEEE Data Compression Conference, Snowbird, Utah, USA, 2017, DOI: 10.1109/DCC.2017.66. (PDF).
Lee Prangnell and Victor Sanchez, "Adaptive Quantization Matrices for HD and UHD Resolutions in Scalable HEVC", IEEE Data Compression Conference, Snowbird, Utah, USA, 2016, DOI: 10.1109/DCC.2016.47. (PDF).
Lee Prangnell, Victor Sanchez and Rahul Vanam, "Adaptive Quantization by Soft Thresholding in HEVC", IEEE Picture Coding Symposium, Cairns, Queensland, Australia, 2015, DOI: 10.1109/PCS.2015.7170042. (PDF).
PhD Title: Visually Lossless Coding for the HEVC Standard — Efficient Perceptual Quantisation Contributions for HEVC (September, 2017)
In my PhD thesis, four perceptual quantisation techniques are proposed for the HEVC standard. These contributions are designed to maximise the levels of perceptual compression that can be applied to raw YCbCr sequences — of various bit depths, resolutions and sampling ratios — without incurring a discernible loss of visual quality. The proposed techniques give rise to significant improvements in terms of bitrate reductions, as measured in kilobits per second. Furthermore, by virtue of the design of the proposed methods, computational complexity is not increased. In terms of experimentation, the following coding efficiency and visual quality metrics are utilised to evaluate each contribution: Bjøntegaard Delta Rate (BD-Rate), PSNR, SSIM and subjective assessments that follow the principles of ITU-T P.910 "Subjective Video Quality Assessment Methods". The proposed perceptual quantisation methods are as follows:
1) High Bit Depth Capable and 4:4:4 Capable JND-Based Coding Block (CB)-Level Perceptual Quantisation;
2) Coding Block (CB)-Level Full Colour Perceptual Quantisation for 4:4:4 Video Data;
3) Coding Unit (CU)-Level Cross-Colour Channel Perceptually Adaptive Quantisation;
4) Transform Coefficient-Level Perceptual Quantisation.
Lee Prangnell, "Visually Lossless Coding for the HEVC Standard: Efficient Perceptual Quantisation Contributions for HEVC", PhD Thesis, Department of Computer Science, University of Warwick, September 2017 (PDF).