An Efficient Image Compression Technique with Dead Zone Quantization Through Wavelet-Based Contourlet Transform with Modified SPIHT Encoding

Abstract

Computer graphics applications especially the applications which produce complex color images produce very large file sizes. Data with very large size creates several problems such as requirement of very large storage space, the need to quickly transmit image data across networks, etc. Hence, image compression has become an active area of research in the field of image processing to reduce file size. Wavelet and curvelet transformations are widely used transformations techniques to carry out compression. But both have their own limitations which affects overall performance of the compression process. This research work focuses on presenting a non-linear image compression technique that compresses image both radically and angularly. Wavelet-based Contourlet Transformation (WBCT) has the potential to approximate the natural images comprising contours and oscillatory patterns. In addition to this transformation deadzone quantization technique is used to eliminate the redundancies in the images. Finally, this technique uses Modified Set Partitioning in Hierarchical Trees (MSPIHT) for efficient encoding process. The experimental results of wavelet-based contourlet transformation with deadzone quantization and MSPIHT are better when compared with existing transformations techniques.
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