Faculty Publications
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Item Gradient-oriented directional predictor for HEVC planar and angular intra prediction modes to enhance lossless compression(Elsevier GmbH journals@elsevier.com, 2018) Shilpa Kamath, S.; Aparna., P.; Antony, A.Recent advancements in the capture and display technologies motivated the ITU-T Video Coding Experts Group and ISO/IEC Moving Picture Experts Group to jointly develop the High-Efficiency Video Coding (HEVC), a state-of-the-art video coding standard for efficient compression. The compression applications that essentially require lossless compression scenarios include medical imaging, video analytics, video surveillance, video streaming etc., where the content reconstruction should be flawless. In the proposed work, we present a gradient-oriented directional prediction (GDP) strategy at the pixel level to enhance the compression efficiency of the conventional block-based planar and angular intra prediction in the HEVC lossless mode. The detailed experimental analysis demonstrates that the proposed method outperforms the lossless mode of HEVC anchor in terms of bit-rate savings by 8.29%, 1.65%, 1.94% and 2.21% for Main-AI, LD, LDP and RA configurations respectively, without impairing the computational complexity. The experimental results also illustrates that the proposed predictor performs superior to the existing state-of-the-art techniques in the literature. © 2018 Elsevier GmbHItem Performance enhancement of HEVC lossless mode using context-based angular and planar intra predictions(Springer, 2020) Kamath, S.; Aparna., P.; Antony, A.Lossless mode of High-Efficiency Video Coding (HEVC), the state-of-the-art video coding standard, can be used for distortion-free reconstruction of the input data for a wide variety of applications. HEVC relies on the usage of efficient intra prediction strategies to achieve superior compression than its predecessor H.264. A large amount of spatial redundancy exists in almost all video sequences due to coherence, smoothness and the inherent correlation within the neighboring pixels. In this paper, a context-based intra prediction scheme is proposed to minimize this local redundancy by identifying the edges and textures to appropriately modify the prediction strategy at the pixel level, without further increase in the computational complexity. The variability in the sum of absolute differences and local pixel intensity values are chosen to derive the context of the nearby region around the target pixel in the planar and angular intra prediction modes respectively. The experimental results validate the superiority of the proposed method over the HEVC anchor and other state-of-the-art techniques in the literature. © 2020, Springer Science+Business Media, LLC, part of Springer Nature.