用于图像压缩的小波系数的上下文模型

结合GlicBawls编码方法的线性预测思想和CALIC编码方法通过量化减少上下文个数的思想，提出了一种新的用于图像压缩的小波系数的上下文模型。它通过量化当前系数的线性预测值形成上下文，进行自适应的算术编码。实验结果表明，利用这种模型获得的无损压缩比高于无损的SPIHT和用于JPEG2000的无损的EBCOT。另外，这种模型充分利用了小波变换的多分辨率性质，能以渐近分辨率(progressive resolution)方式压缩图片，并且它获得的原图片的各个比例尺(scale)的小图片的压缩比也高于EBCOT。

A Context Model of Wavelet Coefficients for Image Compression

Through the combination of the idea of the linear prediction used in the GlicBawls coding scheme and the idea of the quantization taken in the CALIC coding scheme to reduce the number of contexts, a new context model of wavelet coefficients for image compression is proposed. Wavelet coefficients are encoded by the arithmetic encoder, with the contexts formed by quantizing linear prediction values. Experimental results show that the model achieves higher lossless compression rate of image than lossless SPIHT and lossless EBCOT used in JPEG2000. In addition, by exploiting the multiresolution property of wavelet, the model can compress the transformed image for progressive resolution and earn higher compression rate for each scale of the image than EBCOT.