A hidden Markov model-based methodology for intra-field video deinterlacing

This paper presents a new technique of hidden Markov model (HMM) for video deinterlacing. Existing deinterlacing algorithms estimate missing pixels of an absent row in an interlaced frame on a sample-by-sample basis. In contrast, the proposed HMM-based deinterlacing technique adopts an approach of sequence estimation and makes a joint decision on the row of missing pixels as a whole. This allows a more thorough exploitation of the spatial correlation of the image signal. The HMM-based sequence estimation technique is coupled with a number of existing spatial deinterlacing algorithms in the literature to boost their performance. Experimental results show that HMM can significantly improve the deinterlacing results in both PSNR measure and subjective visual quality.