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A frequency adaptive line compression system for mobile display devices

机译:用于移动显示设备的频率自适应线压缩系统

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References(26) The high resolution of the latest high-performance mobile display devices has resulted in an increase in the main frame buffer size and related bandwidth, and this is one of the main causes of a reduced battery life. As a means to solve this problem, one-dimensional line-based compression methods have been studies in order to be implemented in the limited chip design environment of the mobile display driver ICs. Conventional lossless compression techniques do not have a high compression ratio enough for complex images to ensure a sufficient power reduction. On the other hand, a wavelet-based 1D SPIHT method has been studied for use as a lossy-compression method with a high compression ratio while keeping the image quality. However, this method requires large hardware resources due to the performance for iterative calculations and sorting process. This paper proposes a novel wavelet-based lossy compression system that can achieve high compression and image quality while maintaining low complexity. The Frequency Adaptive Line Compression composed of 4-level DWT, horizontal predictive coding for the low-frequency regions, frequency selective zero-zone quantization for high-frequency regions, and frequency component entropy coding. Experimental results confirmed that the proposed technique could achieve a higher compression ratio than the one required of conventional lossless compression methods and could also be implemented with a significantly less complexity than the conventional SPIHT method at the same compression ratio.
机译:参考文献(26)最新的高性能移动显示设备的高分辨率导致主机帧缓冲区大小和相关带宽的增加,这是减少电池寿命的主要原因之一。作为解决该问题的手段,已经研究了基于一维线的压缩方法,以便在移动显示驱动器IC的有限的芯片设计环境中实施。常规的无损压缩技术没有足够高的压缩率以用于复杂图像以确保足够的功率降低。另一方面,已经研究了基于小波的一维SPIHT方法,该方法用作具有高压缩率同时又保持图像质量的有损压缩方法。但是,由于迭代计算和排序过程的性能,此方法需要大量硬件资源。本文提出了一种新颖的基于小波的有损压缩系统,该系统可以在保持较低复杂度的同时实现高压缩率和图像质量。频率自适应线路压缩由4级DWT,针对低频区域的水平预测编码,针对高频区域的频率选择性零区量化和频率分量熵编码组成。实验结果证实,所提出的技术可以实现比常规无损压缩方法所需的压缩率更高的压缩率,并且在相同的压缩率下,与传统的SPIHT方法相比,其实现的复杂性也可以大大降低。

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