Disparity compensated view filtering wavelet and compressive sampling based multi-view image codec

This paper presents a multi-view image codec using disparity compensated lifting based wavelet transform and Compressive Sampling (CS). Disparity compensated view filtering lifting based wavelet transforms are applied to the input multi-view images decomposing the images into their view sub-bands. The dense view is further decomposed into its spatial sub-bands using a wavelet transform. High frequency coefficients are hard threshold to improve and also to control their sparsity. For high frequency sub-bands/views, wavelet-weights are calculated and used to regulate threshold values for those sub-bands/views. The CS algorithm is then used to generate measurements for each resulting sparse sub-band. In the decoder side, the Basis Pursuit method is used to recover the dominant coefficients. An assessment on the energy of the non-dominant coefficients at different compression ratios and their effect on the quality of the reconstructed images are given. Results show that the proposed codec out performs the state of art codecs.