提出了一种基于形态学膨胀操作和差分缩减的DCT 域图像编码方法，该方法根据块内DCT 系数的聚集特性和块间DCT 系数的相似性，使用形态学膨胀算子优化DCT 重要系数的编码，并对重要性检测和符号编码精心设计了上下文自适应算术模型，有效去除了块内、块间DCT 的统计相关性；使用差分缩减方式对各DCT 系数聚类簇的起始位置和稀疏系数进行编码，提高对不重要DCT 系数的编码效率；算法还结合预处理和后处理滤波器，进一步提高编码效率的同时可有效抑制解码图像的方块效应。编码器基于位平面实现，码流具有渐进性。实验结果表明本文算法的编码性能普遍优于目前主流的图像编码器，例如在0.25 bpp 下，Lena 和Barbara 图像的峰值信噪比分别较JPEG2000 提高0.4 dB 和1.7 dB。

A novel DCT domain image coder is proposed according to both intra-block clustering of significant coefficients and inter-block similarity. The algorithm utilizes morphological dilation to extract and encode the clustered significant coefficients, and elaborately adaptive arithmetic models are designed for significance encoding and signencoding, which can eliminate intra-block and inter-block correlation of the DCT coefficients effectively. The difference reduction is used to encode the position of the start pixel in each cluster and scattered coefficients, which can improve the coding efficiency of the insignificant coefficients. Furthermore, the algorithm employs pre-filtering and post-filtering to optimize coding performance and restrain blocking artifacts. As a kind of embedded coder, the algorithm is also rate scalable. Experimental results show that the performance of the new coder is superior to conventional coders. For example, for the Lena and Barbara image at 0.25 bpp, the proposed method outperforms JPEG2000 by 0.4 dB and 1.7dB in peak signal-to-noise ratio, respectively.