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Improvements to SPHIT for lossy image coding

机译:SPHIT的改进,以进行有损图像编码

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Proposes five measures to improve the SPIHT (set partitioning in hierarchical trees) algorithm for lossy image coding. First, a new type of tree with virtual root is introduced to hold more wavelet coefficients. Second, an extra matrix is used to speed up the judgement of the significance of trees. Third, a pre-processing is done to smooth the coefficients before SPIHT encoding. Fourth, some predictable bits are omitted from the encoder output by rearranging the coding procedure. Finally, the quantisation is offset from the middle-point according to the statistics. Our experiments show that these improvements increase the PSNR (peak signal to noise ratio) by up to 5 dB at very low rates, and the average improvement at 0.2-1 bit/pixel is about 0.5 dB for the standard test images used. Despite the performance gain, the computational complexity is also reduced.
机译:提出了五种措施来改进用于有损图像编码的SPIHT(分层树中的集合划分)算法。首先,引入了一种具有虚拟根的新型树,以容纳更多的小波系数。其次,使用额外的矩阵来加快对树木重要性的判断。第三,在SPIHT编码之前进行了预处理以平滑系数。第四,通过重新安排编码过程,从编码器输出中省略了一些可预测的位。最后,根据统计数据,量化会从中点偏移。我们的实验表明,这些改进可以在非常低的速率下将PSNR(峰值信噪比)提高多达5 dB,对于使用的标准测试图像,0.2-1位/像素的平均改进约为0.5 dB。尽管性能有所提高,但计算复杂度也降低了。

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