基于运动补偿和Contourlet变换的视频压缩

Zaid Haitham, Maher K. Mahmood Al-Azawi
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引用次数: 3

摘要

多媒体技术在不同通信设备上的广泛使用增加了传输媒体上的数据流;研究人员试图找到一种有效的方法来压缩数据,以保留存储空间或简化媒体传输。视频压缩技术在许多领域得到了发展,例如(教育和医疗等)。本文将Contourlet变换(CT)与运动补偿技术(MC)相结合,用于视频压缩。CT避免了下采样到高频子带,具有很好的保留图像细节的能力,而且系数也非常稀疏,利用CT与MC结合使用的这些特点,克服了简单帧差技术不能准确地从前一帧合成当前帧的缺点。本文将CT应用于视频的两种帧预测技术:简单帧差(经典方法)和运动补偿。结果表明,带MC的CT具有较好的压缩效果,提高了压缩效率。这些技术在峰值信噪比(PSNR)、压缩比(CR)和每像素比特(BPP)方面获得了结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Video Compression Based on Motion Compensation and Contourlet Transform
The extensive use of multimedia technology by different communication devices increases the data flow over the transmission media; The researchers try to find an efficient way to compress data to preserve a space in storage or to ease transmitting it over media. Video compression techniques are developed in many areas such as (educational and medical, …etc.). Contourlet transform (CT) is used in this paper with a motion compensation technique (MC) for video compression purposes. CT has a good ability to retain image details because it avoids downsampling to a high-frequency sub-band, and the coefficients are also very sparse, by exploiting these features CT is used along with MC which overcomes the disadvantage of not synthesizing the current frame from the previous frame accurately such as in simple frame differencing technique. In this paper CT is applied to two frame’s prediction techniques of the video: simple frame differencing (classical method) and motion compensation. The results show the excellence of CT with MC, where the compression efficiency is improved. Results have obtained with these techniques in terms of Peak Signal to Noise Ratio (PSNR), Compression Ratio (CR) and a Bit Per Pixel (BPP).
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