A Theory for Multiresolution Signal Decomposition: The Wavelet Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Low-Complexity Scalable Image Compression
DCC '00 Proceedings of the Conference on Data Compression
Feature-Preserving Image Coding for Very Low Bit Rates
DCC '01 Proceedings of the Data Compression Conference
Edge and Mean based Image Compression
Edge and Mean based Image Compression
Feature preserving image compression
Pattern Recognition Letters
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Lossy image compression introduces significant loss of picture quality. Many works have been carried out in still image compression techniques, but in most of the techniques quality of the image is not preserved. The quality of the image depends on the features of the image such as edges corners textures etc. In this work, a novel compression technique is proposed with the intent of image quality enhancement using edge information without compromising the compression ratio. Adaptive Wavelet transform is used for both compression and quality enhancement due to its multi resolution characteristics and computing efficiency over a simple wavelet transform. EZW coder is used to encode the wavelet coefficients for enhancing the compression ratio and at the decoder Edge Assisted Wavelet based interpolation (EAWE) is used for enhancing the quality of the image.The experimental results show that the proposed compression system outperforms the existing compression systems in terms of compression ratio and Peak Signal to Noise Ratio. The proposed compression system reduces computing complexity with increased picture quality, so it can be used in remote sensing and mobile applications.