Image compression, as one of the key-enabling technologies in multimedia communications, has been paid much attention in the past decades, where the two key techniques discrete wavelet transform (DWT) and set-partitioning in hierarchical trees (SPIHT) have great influence on its final performance. Due to the proprieties of fast computation, low memory requirement, DWT has been adopted as a new technical standard for still image compression. But it did not make much use of the region information. Although several improved methods have been proposed that adopt direction-adaptive wavelet for using the geometric and spatial information, they still did not consider the texture information. Furthermore, the traditional SPIHT algorithm has the drawbacks of long bits output and time consuming. In this paper, we first propose a new technique named interpolation-based direction-adaptive lifting DWT. It can adaptively choose the best lifting direction and use the Lagrange interpolation technique to make prediction according to its local characteristics. This method makes good use of the image texture features. Then a modified SPIHT coding algorithm is presented. It improves the scanning process and can effectively reduce the coding bits length and running time. Experimental results demonstrate that our method can yield better results than traditional techniques.

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PROJECT TITLE :A Fast Clustering-Based Feature Subset Selection Algorithm for High-Dimensional Data - 2013ABSTRACT:Feature selection involves identifying a subset of the most useful features that produces compatible results as

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