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Abstract:
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Recently, compression techniques using Wavelet Transformation have received great attention for their promising compression ratio, analysis of the temporal and spectral properties of image signals, and flexible representation of non-stationary signals by taking into account the human perception system. With lossless compression, the original image is recovered immediately after decompression. Unfortunately, with images of natural scenes it is rarely possible to obtain error-free compression at a rate beyond 2:1. Much higher compression ratios can be obtained when some errors, which are usually difficult to perceive, are allowed between the decompressed image and the original image. This project investigates wavelet-based lossy compression of 2D still images. This was achieved by performing Discrete Haar Wavelet Transformation (DWT) on an image followed by Entropy Encoding techniques including Embedded Zerotree Wavelet (EZW) and Huffman encoding. |
