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Novel image compression method using edge-oriented classifier and novel predictive noiseless coding method

机译:基于边缘分类器的图像压缩新方法和预测性无噪声编码新方法

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摘要

A new image compression approach is proposed in which variable block size technique is adopted, using quadtree decomposition, for coding images at low bit rates. In the proposed approach, low-activity regions, which usually occupy large areas in an image, were coded with a larger block size and the block mean is used to represent each pixel in the block. To preserve edge integrity, the classified vector quantisation (CVQ) technique is used to code high-activity regions. A new edge-oriented classifier without employing any thresholds is proposed for edge classification. A novel predictive noiseless coding (NPNC) method which exploits the redundancy between neighbouring blocks is also presented to efficiently code the mean values of low-activity blocks and the addresses of edge blocks. The bit rates required for coding the mean values and addresses can be significantly reduced by the proposed NPNC method. Experimental results show that excellent reconstructed images and higher PSNR were obtained.
机译:提出了一种新的图像压缩方法,其中采用可变块大小技术,利用四叉树分解,以低比特率编码图像。在所提出的方法中,通常在图像中占据较大区域的低活性区域以较大的块尺寸进行编码,并且块均值用于表示块中的每个像素。为了保持边缘完整性,分类矢量量化(CVQ)技术用于编码高活性区域。提出了一种不采用任何阈值的面向边缘的新分类器。还提出了一种新颖的预测无噪声编码(NPNC)方法,该方法利用了相邻块之间的冗余性来有效地编码低活动性块的平均值和边缘块的地址。编码的平均值和地址所需的比特率可以通过建议的NPNC方法大大降低。实验结果表明,可以获得良好的重建图像和较高的PSNR。

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