In this paper, adaptive fast matching algorithms are proposed based on the sub-block ordering. Proposed two algorithms for the lossless fast matching only reduce the unnecessary matching complexity of motion estimation in the video coding. By observing the expectation of the sum of absolute differences between the current sub-block and a reference sub-block in the pixel domain, we derive an absolute difference model which indicates the relationship between the sub-block distortion and the intra sub-block complexity. In the same way, we consider the inter complexity among all sub-block with the absolute difference model. From the absolute difference model, we derive the transform coefficient model for measuring the sub-block complexity in the transform domain based on the Parseval's theorem. Experimental results show the proposed fast matching algorithms reduce the unnecessary complexity of the full search algorithm about 20% on average without any loss of image quality.

H.264/AVC is adopted to a variety of applications because of its high compression efficiency. The high coding efficiency of H.264 is mainly acquired from the newly allowed prediction schemes including variable block mode. However these schemes require a high complexity to select the optimal mode. In this paper, to reduce this large computational burden, an adaptive fast inter mode decision algorithm using dynamic threshold technique for H.264/AVC P slices is proposed. In many sequences, the modes for large block such as SKIP and P16times16 occupy the most portions optimal modes. This fact gives us very important clue. The experimental results show that the proposed mode decision algorithm can reduce entire encoding time effectively by about 42.5% only without noticeable PSNR loss.

Most of previous interpolation methods cannot consider the sufficient edge information, and that result in unwanted artifacts. To remove or reduce the problem effectively, we propose a new image interpolation scheme based on Weighted Edge Pattern Analysis (WEPA). Proposed algorithm finds the accurate edge direction at current pixel by using the Spatial Direction Vector (SDV) which is result of WEPA of the neighboring pixels. After direction refinement, we interpolate the current pixel along obtained final SDV direction. Experimental results show that the proposed method exhibits a better performance than previous methods in both objective and subjective results with a variety of still images.

We intuitively derive DC and AC constraints to model local block complexities using ordered Hadamard transform from pixel based gradient method. For lossless motion estimation (ME), using (1), we obtain the optimized search order in the matching error calculation by descending order of local constraints using sum of these two constraints. LBC(k) = ACSum(k) + |DCMB- DCLB(k)|.

In this paper, we propose a new interpolation algorithm based on edge-weighted method, which improves the bilinear interpolation method. The conventional bilinear interpolation has a serious problem which is the blurring artifact crumbling the edge components. In this paper, the edge-weighted image (EWI) interpolation algorithm, which enhances the edge components, is proposed. In the experimental results, not only did the EWI interpolation exhibit much better subjective qualities of the images than conventional bilinear interpolation, but also improved objective qualities.

In this paper, an efficient algorithm for the fast full search is proposed to reduce the computational cost of block matching algorithm for motion estimation in video coding. Based on the characteristic of the pixel histogram for the current macroblock, the proposed fast matching method adaptively sorts the matching order pixel-by-pixel for each macroblock. Also, partial distortion elimination (PDE) method was used to terminate the improper candidate macroblocks early after the process of multilevel successive elimination algorithm (MSEA). The varieties of test sequences were simulated and the experimental results demonstrate that the proposed algorithm achieves less computational load.

This paper presents a novel intra deinterlacing algorithm (NID) based on content adaptive interpolation. The NID consists of three steps: pre-processing, content classification, and content adaptive interpolation. There are also three main interpolation methods in our proposed NID, i.e. modified edge-based line averaging (M-ELA), gradient directed interpolation (GDI), and window matching method (WMM). Each proposed method shows different performances according to spatial local features. Therefore, we analyze the local region feature using the gradient detection and classify each missing pixel into four categories. And then, based on the classification result, a different de-interlacing algorithm is activated in order to obtain the best performance. Experimental results demonstrate that the NID method performs better than previous techniques.