Hybrid image compression scheme based on wavelet transform and adaptive context modeling

P. Bao, Xiaolin Wu
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Abstract

Summary form only given. We propose a hybrid image compression scheme based on wavelet transform, HVS thresholding and L/sub /spl infin//-constrained adaptive context modelling. This hybrid system combines the strengths of the wavelet transform, the HVS thresholding and the adaptive context modelling to result in a near optimal compression scheme. The wavelet transform is very powerful in localizing the global spatial and frequency correlation. The HVS model-based thresholding is designed to exploit and eliminate the wavelet coefficients insensitive to the human visual system. The context-based modelling is superior in decorrelating the local redundancy. In the scheme, the image is first decomposed into the multiresolution subimages using the orthogonal wavelet transform; each subimage corresponds to a octave band in the wavelet decomposition. The coefficients in the high-pass octave bands of the wavelet transform are then quantized through HVS frequency- and spatial model-based thresholding and vector quantization into wavelet decomposition with only significant coefficients to the HVS retained. In this HVS quantized wavelet decomposition, the coefficients insignificant to the human visual system are normalized to zero and the global spatial and frequency correlation are exploited and removed. Then the quantized subimages in the low-pass band and the remaining high-pass octave bands of each octave level are processed using the L/sub /spl infin//-constrained CALIC to de-correlate the local redundancy. It is demonstrated that the hybrid scheme is one of the best compression schemes in achieving the excellent compression rates and competitive PSNR while maintaining a small visual distortion. In comparing with the original CALIC, we were able to increase the PSNR by 0.65 dB or more and obtain bit rates 15 percent lower than the latter. We were also able to obtain competitive PSNR results against the best wavelet coders, while maintaining a smaller visual distortion. In particular, the wavelet CALIC was able to obtain 1.34 to 7.84 dB higher PSNR on the standard ISO test benchmarks than the SPIHT, one of the best wavelet coder.
基于小波变换和自适应上下文建模的混合图像压缩方案
只提供摘要形式。我们提出了一种基于小波变换、HVS阈值和L/sub /spl infin// /约束自适应上下文建模的混合图像压缩方案。该混合系统结合了小波变换、HVS阈值和自适应上下文建模的优势,从而产生了接近最优的压缩方案。小波变换在全局空间相关性和频率相关性的局部化方面非常有效。基于HVS模型的阈值分割旨在利用和消除对人类视觉系统不敏感的小波系数。基于上下文的建模在去相关局部冗余方面具有优势。该方案首先利用正交小波变换将图像分解成多分辨率子图像;在小波分解中,每个子图像对应一个倍频带。然后通过基于HVS频率和空间模型的阈值化和矢量量化将小波变换中高通倍频带的系数量化为小波分解,只保留对HVS有意义的系数。在HVS量化小波分解中,将对人类视觉系统不重要的系数归一化为零,利用并去除全局的空间和频率相关性。然后利用L/sub /spl infin//-约束CALIC对低通频带和剩余高通频带的量化子图像进行处理,去除局部冗余。结果表明,混合压缩方案是一种较好的压缩方案,能在保持较小的视觉失真的同时,获得较好的压缩率和有竞争力的PSNR。与原始CALIC相比,我们能够将PSNR提高0.65 dB或更高,并获得比后者低15%的比特率。我们还能够获得与最佳小波编码器相比具有竞争力的PSNR结果,同时保持较小的视觉失真。特别是,在标准ISO测试基准上,小波CALIC能够获得比SPIHT高1.34至7.84 dB的PSNR, SPIHT是最好的小波编码器之一。
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