A Mixed Parallel and Pipelined Efficient Architecture for Intra Prediction Scheme in HEVC

Poola, Lakshmi; Aparna, P.

doi:10.1080/02564602.2020.1841686

Cited by 6 publications

(1 citation statement)

References 17 publications

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“…CU is composed of the three Coding Blocks (CBs) Luminance (Y), Blue Chroma (Cb), and Red Chroma for a color image (Cr). CB is sufficient for determining prediction type, but it may need to be more significant to store Motion Vectors (MV) inter prediction or intra-prediction mode.The Prediction Unit (PU) for each CU must be determined [4][5][6][7] . Each PU has one of 35 distinct prediction modes.…”

Section: Introductionmentioning

confidence: 99%

High Speed Coding Unit Depth Identification Based on Texture Image Information Using SVM

Hari,

Batta

2024

Def. Sc. J.

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High-Efficiency Video Coding (HEVC/H.265) is a new video coding standard with half the bit rate of its predecessor, Advanced Video Coding (AVC/H.264). AVC/H.264 uses macroblocks, processing units between 4×4 and 16×16 pixels in size. H.265 uses Coding Tree Units (CTUs), a more complicated block structure that lets images be as large as 64×64 pixels. However, changing from macroblocks to coding tree units is essential for H.265 to become more efficient. Using the quadtree structure to divide the Coding Unit (CU) makes it harder for HEVC to find the optimal rate distortion. This paper presents a Support Vector Machine (SVM)-based method for finding the fastest coding unit division in intra-prediction HEVC without compromising compression efficiency. All partitions of CTU are assessed using five characteristics: Standard Deviation (SD), Root Mean Square Error (RMSE), Sub CU Complexity Difference (SCCD), Directional Complexity (DC), and Quantization Parameter (QP) to optimize the intra-prediction of HEVC in all intra-configurations. Simulations have been carried out to estimate the performance of the proposed machine learning-based algorithm using test sequences with different resolutions. Simulation results have shown that combining directional complexity and standard deviation gives a more accurate classification. SVM has been used to separate split-unsplit samples, and the standard rate-distortion optimization technique has been used to separate samples that are hard to separate. The results have shown a reduction of 67.44% in encoding time with a slight increase in bit rate.

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Section: Introductionmentioning

confidence: 99%