Soonjong Jin

Novel Intra Deinterlacing Algorithm Using Content Adaptive Interpolation

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.

Fine directional de-interlacing algorithm using modified Sobel operation

This paper presents an intra field de-interlacing algorithm for spatial edge preserving using the detection of accurate edge direction. Conventional intra field de-interlacing algorithms determine the edge direction at the pixel or half pixel level, so that they can be highly sensitive to noise and lead to image degradation. In this paper, the proposed algorithm first considers the edge tendency in the edge region, and then candidate direction vectors (CDVs) are selected using the modified Sobel operation through the edge tendency. Finally, the CDVs are adaptively applied to interpolate the lost pixel in the edge region. Experimental results show that the proposed algorithm performs well with a variety of still and moving images compared with conventional intra field algorithms in the literature.

Adaptive Fast Full Search Algorithm using Partitioned Region and Optimized Search Order

In this paper, an adaptive fast full search algorithm is proposed. The fast full search algorithm only reduces the complexity of the block matching algorithm used for motion estimation of video coding. Since the spiral search method used in conventional algorithms starts searching at the center of the search range and then moves the candidate position to estimate motion pixel by pixel, it is best for slow motion pictures. The spiral search method usually has better performance than a full search using the raster scan search method. However, the proposed algorithm is best for sequences which have numerous rapid motions. In the first of two proposed stages, using a predicted motion vector, we determine the region search order based on gradient distances among all regions after partitioning the search range. In the second stage, by means of region order, the matching error of all candidate positions is calculated in an optimized search order which is derived from the local image complexity of the current macro-block to search. The proposed algorithm, based on a multilevel successive elimination algorithm, reduces the complexity of motion estimation up to 96.6% on average, compared with the conventional full search algorithm without any loss of image.

Adaptive partial distortion search algorithm using histogram-based sorting

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.

Hadamard Transform Based Fast Partial Distortion Elimination Algorithm for Lossless and Lossy Motion Estimation

In this paper, based on ordered Hadamard transform and its probability model, a fast partial distortion elimination algorithm is proposed. For lossless motion estimation, we propose DC and AC constraints to model local block complexities using ordered Hadamard transform. Using sum of these constraints, we obtain the optimized search order in the matching error calculation by descending order of local constraints. And for the fast search in lossy motion estimation, using probability model which is approximated by the cumulative distribution function of constraints, we also propose the accurate total matching error prediction algorithm without all matching error calculations. The proposed algorithm reduces the motion estimation complexity up to 92.4% for lossy motion estimation and 89.4% for lossless motion estimation on average, compared with the conventional full search algorithm.

New fast full search algorithms using DCT coefficients

This paper presents new fast full search motion estimation (ME) algorithms using discrete cosine transform (DCT) coefficients as a gradient magnitude. The proposed algorithms use intra DCT coefficients to arrange the matching scan order. The proposed algorithm 1 uses some of DCT coefficients based on the relationship between gradient magnitude and DCT coefficients, and the proposed algorithm 2 uses all of DCT alternating current (AC) coefficients in order to predict the fluctuation of a block. The proposed algorithms significantly reduce the computational complexity of the conventional algorithm while maintaining the visual quality of video.

Enhanced Edge-Weighted Image Interpolation Algorithm

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.

Human Perception of Asymmetrical 3-D Inputs

The 3DTV services can be seen as a general case of the multi-view video that has been receiving a significant attention lately. However, the key factors that influence the success of 3DTV are the availability of content, the ease of use, the quality of contents, and the reduction of cost. This paper deals primarily with the perceptual improvement in image quality, especially based on human factors. An optimal asymmetrical coding method for binocular and multi-view images is presented. The quantitative value of asymmetrical rate to maintain optimized subjective image quality is explored. Also we analyze how edges of 2D images affect on 3D perceptions and propose an edge-preserving algorithm to perform perceptual improvements. Experimental results demonstrate that the proposed algorithm enhances subjective image quality much better than conventional methods.

Adaptive Fast Inter Mode Decision Algorithm Using Dynamic Threshold Technique for H.264/AVC P Slices

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 P16×16 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.

Human Perception of Asymmetrical Three-Dimensional Image

The 3DTV services can be seen as a general case of the multi-view video that has been receiving a significant attention lately. However, the key factors that influence the success of 3DTV are the availability of content, the ease of use, the quality of contents, and the reduction of cost. This paper deals primarily with the perceptual improvement in image quality, especially based on human factors. An optimal asymmetrical coding method for binocular and multi-view images is presented. The quantitative value of asymmetrical rate to maintain optimized subjective image quality is explored. Also we analyze how edges of 2D images affect on 3D perceptions and propose an edge-preserving algorithm to perform perceptual improvements. Experimental results demonstrate that the proposed algorithm enhances subjective image quality much better than conventional methods.