Quantization Noise Reduction Using Wavelet Thresholding for VariousCoding

We propose a nonlinear, wavelet-based method to eeciently improve the performance of various coding schemes for lossy image data compression. Coarse quantization of the transform coeecients often results in some undesirable artifacts, such as ringing eeect, contouring eeect and blocking eeect, especially at very low bit rate. The decoding can be viewed as a typical statistical estimation problem of reconstructing the original image signal from the decompressed image, a noisy observation, using the classical signal processing model of \signal plus additive noise". We perform the wavelet-domain thresholding on the decompressed image to attenuate the quantization noise eeect while maintaining the relatively sharp features (e.g. edges) of the original image. Experimental results show that de-noising using the undecimated discrete wavelet transform achieves better performance than using the orthonormal discrete wavelet transform, with an acceptable computational complexity (O(MN log 2 (MN)) for an image of size M N). Both the objective quality and the subjective quality of the reconstructed image are signiicantly improved with the reduction of coding artifacts. In addition, dithering technique can be embedded in the encoding scheme to achieve further improvement of the visual quality.