Model-based edge reconstruction for low bit-rate wavelet-compressed images

At low bit rates, wavelet-based image coding is superior to most traditional block-based methods in terms of visibility and severity of coding artifacts in coded images. However, the compressed images still suffer from obvious distortions around sharp edges, which are perceptually objectionable. In order to improve image quality for low bit-rate wavelet-based image coding, we proposed a model-based edge-reconstruction algorithm for recovering the lossy edges in coded images. Our approach applies a general model to represent varieties of edges existing in an image. Based on this model, the edge degradation process due to quantization errors of wavelet coefficients is analyzed with the characterization of two kinds of artifacts at edges. We develop two operations, model-based edge approximation and Gaussian smoothing, to reconstruct distorted edges by reducing both artifacts respectively. The proposed method is able to improve image quality in terms of both visual perception and image fidelity (peak signal-to-noise ratio) for most images coded by wavelet-based methods at low bit-rates.