Hsieh Ming-Shing, Tseng Din-Chang
Inst. of Comput. Sci. & Inf. Eng., Nat. Central Univ., Chung-li, Taiwan.
IEEE Trans Syst Man Cybern B Cybern. 2003;33(3):509-13. doi: 10.1109/TSMCB.2003.811131.
Wavelet image decomposition generates a hierarchical data structure to represent an image. Recently, a new class of image compression algorithms has been developed for exploiting dependencies between the hierarchical wavelet coefficients using zerotrees. This paper deals with a fuzzy inference filter for image entropy coding by choosing significant coefficients and zerotree roots in the higher frequency wavelet subbands. Moreover, an adaptive quantization is proposed to improve the coding performance. Evaluating with the standard images, the proposed approaches are comparable or superior to most state-of-the-art coders. Based on the fuzzy energy judgment, the proposed approaches can achieve an excellent performance on the combination applications of image compression and watermarking.
小波图像分解生成一种分层数据结构来表示图像。最近，已经开发出一类新的图像压缩算法，用于利用零树来利用分层小波系数之间的相关性。本文通过在高频小波子带中选择重要系数和零树根来处理用于图像熵编码的模糊推理滤波器。此外，还提出了一种自适应量化方法来提高编码性能。通过对标准图像进行评估，所提出的方法与大多数最先进的编码器相当或更优。基于模糊能量判断，所提出的方法在图像压缩和水印的组合应用中可以实现优异的性能。
