High quality image reconstruction via non-local collaborative estimation for wireless image/video softcast

For wireless scenarios where the channel condition fluctuates unpredictably, a novel image/video communication scheme, named SoftCast, was recently proposed to provide graceful quality degradation and competitive performance simultaneously. Unlike conventional approaches, SoftCast decorrelates input images by a transform and modulates the coefficients directly to a dense constellation for transmission, leaving out the conventional quantization, entropy coding and channel coding. The transmission is lossy in nature, with its noise level commensurate with the channel condition. To reconstruct images from the received noisy data, SoftCast employs a linear least-square estimator (LLSE), but it tends to produce annoying reconstruction artifacts. This paper proposes a high-quality image reconstruction algorithm for SoftCast, employing a collaborative estimator to utilize both the local correlation and non-local similarity within images. Experimental results show that the proposed method outperforms the existing SoftCast scheme, achieving remarkable improvement in the objective and subjective qualities of the reconstruction images.