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首页> 外文会议>International Conference on Computer, Communication and Signal Processing >Analysis on Various Approaches of Complexity Reduction for Intra Prediction Modes in High Efficiency Video Coding
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Analysis on Various Approaches of Complexity Reduction for Intra Prediction Modes in High Efficiency Video Coding

机译：高效视频编码中帧内预测模式复杂性降低的各种方法分析

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摘要

Video compression is an image processing technique that removes redundant data while encoding the video content. The purpose of compressing the video is to save storage space and to reduce transmission bandwidth. High Efficiency Video Coding (HEVC) is the latest compression standard available in the market and has better efficiency than its predecessor H.264/AVC. But the complexity arises during the split of the Coding Unit (CU) to form Prediction Unit (PU) and to choose the best prediction modes of the 35 prediction modes available. Hence, there is a need to reduce the computational complexity. This paper presents the different algorithms available in the literature to minimize the number of prediction modes using heuristic, machine learning and deep learning approaches. The performance analysis from the studies are tabulated and the observation made from the study is that the algorithms using deep learning techniques help to get enhanced encoding time reduction with little compromise on Bjontegaard delta bit-rate (BDBR) comparatively. The available reference software HEVC TEST Model (HM) was used as standard to study different video quality metrics such as peak signal-to-noise ratio (PSNR), Bit Rate (BD rate), and encoding time.
机译：视频压缩是一种图像处理技术，可在编码视频内容时去除冗余数据。压缩视频的目的是节省存储空间并减少传输带宽。高效视频编码（HEVC）是市场上可用的最新压缩标准，比其前任H.264 / AVC具有更好的效率。但是，在编码单元（CU）的分开期间形成复杂性以形成预测单元（PU）并选择可用的35预测模式的最佳预测模式。因此，需要降低计算复杂性。本文介绍了文献中可用的不同算法，以最大限度地减少使用启发式，机器学习和深度学习方法的预测模式的数量。研究的性能分析表明，从研究中进行的观察结果是使用深度学习技术的算法有助于获得增强的编码时间减少，在Bjontegaard Delta比特率（BDBR）上略微妥协。可用的参考软件HEVC测试模型（HM）用作标准，以研究不同的视频质量指标，例如峰值信噪比（PSNR），比特率（BD速率）和编码时间。

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