Journal of Mathematical Imaging and Vision
Training methods for image noise level estimation on wavelet components
EURASIP Journal on Applied Signal Processing
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We propose a nonlinear, universal method based on wavelet thresholding to efficiently improve the performance of various coding schemes. Coarse quantization of the transform coefficients often results in some undesirable artifacts, such as ringing effect, contouring effect and blocking effect, especially at very low bit rate. We perform the wavelet-domain thresholding on the decompressed image to attenuate the quantization noise effect while maintaining the relatively sharp features (e.g. edges) of the original image. Both the objective quality and the subjective quality of the reconstructed image are significantly improved with the reduction of coding artifacts. Experimental results show that de-noising using the undecimated discrete wavelet transform (DWT) achieves better performance than using the orthonormal DWT, with an acceptable computational complexity (O(MN log/sub 2/ (MN)) for an image of size M/spl times/N).