This paper introduced a spatial-temporal contentadaptive algorithm, which can precisely select an appropriate interpolation technique for high-quality deinterlacing according to the spectral, edge-oriented and statistical features of local video content. Our algorithm employs a linear-phase statisticaladaptive vertical-temporal filter to deal with generic video scenes and adopts a modified edge-based line-averaging interpolation to efficiently recover moving edges. In addition, annoying flickering artifacts are efficiently suppressed by a flickering detection and a field-averaging filter. As a result, our algorithm outperforms other non-motion compensated methods in terms of objective PSNR and reveals more impressive subjective visual quality.

In this paper we introduce a novel algorithm that can detect local features and choose a proper interpolation method for de-interlacing. An edge is a high frequency pattern with certain direction which is a noticeable feature in video sequences. We proposed a wide range ELA (WRELA) algorithm capable of accurately detecting edge directions. The edge direction can be acquired from an optimized procedure. Finally, we can interpolate the missing pixel in the edge with the direction which has the highest correspondence. We also implement the architecture of the proposed de-interlacing algorithm by UMC 0.18 μm technology. This design is capable of real-time deinterlacing for high definition 720i sequences with the clock speed running at 54 MHz, and the gate count is acceptable. Experimental results show that our proposed de-interlacer provides not only high objective performance in terms of PSNR but also impressive visual quality especially for edges.