Edge-preserving spatial deinterlacing for still images using block-based region classification

Abstract

A number of deinterlacing algorithms have been proposed, however, they have some drawbacks: blocking artifacts or blurring around edges. To overcome these drawbacks, we propose an edge-preserving spatial deinterlacing algorithm using block-based region classification. The proposed algorithm classifies blocks of a line into three types of regions: connected, shaded, and unshaded. It finds the best matched block and interpolates lines block by block. Experimental results gives better performance than conventional spatial deinterlacing algorithms, especially near edges, in terms of the peak signal to noise ratio (PSNR) and subjective visual quality.