Multi-resolution, perceptual and compressive sampling based image codec

Abstract

Direct application of compressive sampling in coding wavelet high frequency coefficients of an image, is unpleasantly deteriorating the quality of the reconstructed image. This is due to an error introduced by many high frequency coefficients that have small but nonzero values. In this paper, a novel multi-resolution image coding scheme using compressive sampling and perceptual weights is presented that significantly improves the quality of the reconstructed images by setting the coefficients with small values to zero using two different hard thresholding operators. The proposed codec applies a wavelet transform on the input image and decorrelates the image into its frequency subbands. Baseband coefficients are lossless coded to preserve their visually important information. High frequency subbands' coefficients are hard threshold to improve and also to control their sparsity. Perceptual-weights for different wavelet subbands are calculated and used to adjust threshold values for different subbands. Compressive sampling algorithm is used to generate measurements for each resulting sparse subband. Measurements for each subband are then cast to an integer and arithmetic coded. In the decoder side, the Basis Pursuit method is used to recover the coefficients. Empirical values for the observation factor for best coding performance of the codec, using standard test images, were first determined. The performance of the codec was assessed using standard test images. Results show that the application of perceptual weights in regulating threshold values significantly improves the coding performance of the codec. (4 pages)