• ETRI Journal
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• v.35 no.6
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• pp.1021-1028
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• 2013

Automatic facial expression recognition is a widely studied problem in computer vision and human-robot interaction. There has been a range of studies for representing facial descriptors for facial expression recognition. Some prominent descriptors were presented in the first facial expression recognition and analysis challenge (FERA2011). In that competition, the Local Gabor Binary Pattern Histogram Sequence descriptor showed the most powerful description capability. In this paper, we introduce hybrid facial representations for facial expression recognition, which have more powerful description capability with lower dimensionality. Our descriptors consist of a block-based descriptor and a pixel-based descriptor. The block-based descriptor represents the micro-orientation and micro-geometric structure information. The pixel-based descriptor represents texture information. We validate our descriptors on two public databases, and the results show that our descriptors perform well with a relatively low dimensionality.

• Proceedings of the IEEK Conference
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• 2008.06a
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• pp.807-808
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• 2008

In H.264, sub-pel motion estimation (ME) has strong effect when coding video sequences. 1/4-pel performs better at low bitrate while 1/8-pel gives better results at high bitrate. In this paper, a variable sub-pixel motion vector resolution based on block mode motion estimation method is proposed. Experiment results show that the proposed method can take the advantage of 1/4-pel at low bitrate and 1/8-pel at high bitrate. In addition to that, time is reduced from 14% to 53% compared to KTA1.3 with 1/8-pel motion vector (MV) resolution.

• Journal of Broadcast Engineering
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• v.10 no.4 s.29
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• pp.576-586
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• 2005

This paper presents a new estimation and representation of a disparity map for stereo-based video communication systems. Several pixel-based and block-based algorithms have been proposed to estimate the disparity map. While the pixel-based algorithms can achieve high accuracy in computing the disparity map, they require a lost of bits to represent the disparity information. The bit rate can be reduced by the block-based algorithm, sacrificing the representation accuracy. In this paper, the block enclosing a distinct edge is divided into two regions and the disparity of each region is set to that of a neighboring block. The proposed algorithm employs accumulated histograms and a neural network to classify a type of a block. In this paper, we proved that the proposed algorithm is more effective than the conventional algorithms in estimating and representing disparity maps through several experiments.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.32 no.2C
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• pp.124-131
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• 2007

This paper presents a block loss recovery technique for the image block data corrupted by transmission losses through the employment of fine directional interpolation (FDI). The proposed algorithm introduces a spatial direction vector (SDV). The SDVs are extracted from the edge information of the neighboring image data. Subsequently, the SDVs are adaptively applied to interpolate lost pixels on a pixel-by-pixel basis. This approach improves the capability to more reliably recover high-detailed contents in the corrupted block. Experimental results demonstrate that the FDI method performs better as compared to previous techniques.

• Journal of the Institute of Electronics Engineers of Korea TE
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• v.39 no.3
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• pp.28-34
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• 2002

This paper describes embedding non-linearly watermark data in the components of pixel intensities in the spatial domain of an image. The principle of the proposed method is that when an image is segmented regularly to the blocks, the pixels of the block have the non-linear properties without any similarity. That is, for the block with strong non-linear property human can't feel the visual different to the modified pixel values, on the other hand for the block with weak non-linear property human can feel the visual different to the a little modified pixel values. Thus we could embed the watermark data according to the non-linear property of the blocks. As the result of the simulation, against some general image processing attacks our algorithm could keep robust and be responsible for the copyright certainly. 

• Journal of the Korean Institute of Telematics and Electronics B
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• v.31B no.2
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• pp.45-55
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• 1994

In this paper, a block based image approximation technique using the Self Affine System(SAS) from the fractal theory is suggested. Each block of an image is divided into 4 tiles and 4 affine mapping coefficients are found for each tile. To find the affine mapping cefficients that minimize the error between the affine transformed image block and the reconstructed image block, the matrix euation is solved by setting each partial differential coefficients to aero. And to ensure the convergence of coding block. 4 uniformly partitioned affine transformation is applied. Variable block size technique is employed in order to applynatural image reconstruction property of fractal image coding. Large blocks are used for encoding smooth backgrounds to yield high compression efficiency and texture and edge blocks are divided into smaller blocks to preserve the block detail. Affine mapping coefficinets are found for each block having 16$\times$16, 8$\times$8 or 4$\times$4 size. Each block is classified as shade, texture or edge. Average gray level is transmitted for shade bolcks, and coefficients are found for texture and edge blocks. Coefficients are quantized and only 16 bytes per block are transmitted. Using the proposed algorithm, the computational load increases linearly in proportion to image size. PSNR of 31.58dB is obtained as the result using 512$\times$512, 8 bits per pixel Lena image.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.36 no.11C
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• pp.662-672
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• 2011

This paper presents a new edge-preserving algorithm and its VLSI architecture for block artifact reduction. Unlike previous approaches using block classification, our algorithm utilizes pixel classification to categorize each pixel into one of two classes, namely smooth region and edge region, which are described by the edge-preserving maps. Based on these maps, a two-step adaptive filter which includes offset filtering and edge-preserving filtering is used to remove block artifacts. A pipelined VLSI architecture of the proposed deblocking algorithm for HD video processing is also presented in this paper. A memory-reduced architecture for a block buffer is used to optimize memory usage. The architecture of the proposed deblocking filter is prototyped on FPGA Cyclone II, and then we estimated performance when the filter is synthesized on ANAM 0.25 ${\mu}m$ CMOS cell library using Synopsys Design Compiler. Our experimental results show that our proposed algorithm effectively reduces block artifacts while preserving the details.

• Proceedings of the IEEK Conference
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• 2000.07b
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• pp.885-888
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• 2000

Among the previous searching methods, there are the typical methods such as full search and three-step search, etc. Block motion estimation using exhaustive search is too computationally intensive. To apply in practice, recently proposed fast algorithms have been focused on reducing the computational complexity by limiting the number of searching points. According to the reduction of searching points, the quality performance is aggravated in those algorithms. In this paper, We present a fast and efficient search algorithm for block motion estimation that produces better quality performance and less computational time compared with a three-step search (TSS). Previously the proposed Two Step Search Algorithm (TWSS) by Fang-Hsuan Cheng and San-Nan sun is based on the ideas of dithering pattern for pixel decimation using a part of a block pixels for BMA (Block Matching Algorithm) and multi-candidate to compensate quality performance with several locations. This method has good quality performance at slow moving images, but has bad quality performance at fast moving images. To resolve this problem, the proposed algorithm in this paper considers spatial and temporal correlation using neighbor and previous blocks to improve quality performance. This performance uses neighbor motion vectors and previous motion vectors in addition, thus it needs more searching points. To compensate this weakness, the proposed algorithm uses statistical character of dithering matrix. The proposed algorithm is superior to TWSS in quality performance and has similar computational complexity

• Journal of Korea Multimedia Society
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• v.13 no.2
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• pp.185-195
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• 2010

In this paper, we propose an efficient inter and intra prediction algorithms for fast encoding of H.264/AVC. First, inter prediction mode decision method decides early using temporal/spatial correlation information and pixel direction information. Second, intra prediction mode decision method selects block size judging smoothness degree with inner/outer pixel value variation and decides prediction mode using representative pixel and reference pixel. Lastly, adaptive motion search region restriction sets search region using mode information of neighboring block and predicted motion vector. The experimental results show that proposed method can achieve about 18~53% reduction compared with the existing JM 14.1 in the encoding time. In RD performance, the proposed method does not cause significant PSNR value losses while increasing bitrates slightly.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.4C
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• pp.355-362
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• 2009

A new intra field deinterlacing algorithm with edge map in the image block is introduced. Conventional deinterlacing methods usually employ edge-based line average algorithm within pixel-by-pixel approach. However, it is sensitive to variation of intensity. To reduce this shortcoming, we proposed edge direction vector computed by edge map, and also its interpolation technique. We first introduce an edge direction vector, which is computed by Sobel mask, so that finer resolution of the edge direction can be acquired. The proposed edge direction vector oriented deinterlacer operates by identifying small pixel variations in five orientations, while weighted averaging to estimate missing pixel. According to the edge direction of the direction vector, we calculate weights on each edge direction. These weight values multiplied by the candidate deinterlaced pixels in order to successively build approximations of the deinterlaced sequence.