基于层次DCT变换的嵌入式图像编码方法

提出了基于层次DCT变换的嵌入式零树编码方法.尽管嵌入式零树编码方法首先使用于小波变换,但结合层次DCT结构,基于DCT的嵌入式零树编码方法依然可以取得很好的压缩效果.层次DCT首先将输入图像划分为8×8的图像块,然后对这些图像块进行第1层的DCT变换.因为相邻的DC系数高度相关,并且特别影响低比特率时的压缩效果,因此第2层的DCT变换被应用于这些DC系数上.这一过程重复进行,直到最后一步为止.因为DCT图像块通过重新组织,可以看做是类似于小波变换的子带结构,因此可以使用嵌入式零树编码方法.实验结果表明,所提出的基于层次DCT变换的嵌入式零树编码方法具有较低的计算复杂性和很好的压缩效果.例如,与JPEG相比较,对标准的512×512Lena图像的而言,此方法可获得0.8dB～1.7dB的提高.为了去除低比特率压缩所引起的图像块效应,提出了基于Sobel算子的后处理算法.实验结果显示,所提出的后处理算法对解码后的图像不论是主观效果还是客观效果均有提高.

Embedded Image Coding Based on Hierarchical Discrete Cosine Transform

In this paper, we illustrate that the zerotree quantizer developed originally for wavelet compression can be effectively applied to Discrete Cosine Transform (DCT) in a hierarchical way. In this Hierarchical DCT (HDCT), the input image is partitioned into a number of 8×8 blocks and a first level DCT is used to each of these blocks individually. As DC coefficients of DCT neighboring blocks are highly correlated and particularly pronounced to obtain the compression results at low bit rates, another level DCT is applied to only DC coefficients re-organized as 8×8 blocks. This procedure is repeated until the last step is reached. All the HDCT coefficients within a DCT block are then rearranged into a sub-band structure in which the zerotree quantizer can be employed. The proposed algorithm yields a fully embedded, low-complexity coder with competitive PSNR performance. For example, when compared with the baseline JPEG on the 512×512 standard image "Lena”, it gains 0.8 dB～1.7 dB. In order to remove the blocking effects in the reconstructed images, especially at low bit rates, a simple and efficient method based on Sobel operators is also developed. Experimental results show that the proposed deblocking method works well and enhances decoding for decompressed images both objectively and subjectively.