• Journal of the Korean Society of Radiology
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• v.14 no.3
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• pp.211-220
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• 2020

High-quality computed tomography (CT) images enable precise lesion detection and accurate diagnosis. A lot of studies have been performed to improve CT image quality while reducing radiation dose. Recently, deep learning-based techniques for improving CT image quality have been developed and show superior performance compared to conventional techniques. In this study, a super-resolution convolutional neural network (SRCNN) model was used to improve the spatial resolution of CT images, and image quality according to the hyperparameters, which determine the performance of the SRCNN model, was evaluated in order to verify the effect of hyperparameters on the SRCNN model. Profile, structural similarity (SSIM), peak signal-to-noise ratio (PSNR), and full-width at half-maximum (FWHM) were measured to evaluate the performance of the SRCNN model. The results showed that the performance of the SRCNN model was improved with an increase of the numbers of epochs and training sets, and the learning rate needed to be optimized for obtaining acceptable image quality. Therefore, the SRCNN model with optimal hyperparameters is able to improve CT image quality.

• The KIPS Transactions:PartB
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• v.11B no.6
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• pp.653-660
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• 2004

Object-oriented analysis-synthesis coding (OOASC) subdivides each image of a sequence into a number of moving objects and estimates and compensates the motion of each object. It employs a motion parameter technique for estimating motion information of each object. The motion parameter technique employing gradient operators requires a high computational load. The main objective of this paper is to present efficient motion parameter estimation techniques using the hierarchical structure in object-oriented analysis-synthesis coding. In order to achieve this goal, this paper proposes two algorithms : hybrid motion parameter estimation method (HMPEM) and adaptive motion parameter estimation method (AMPEM) using the hierarchical structure. HMPEM uses the proposed hierarchical structure, in which six or eight motion parameters are estimated by a parameter verification process in a low-resolution image, whose size is equal to one fourth of that of an original image. AMPEM uses the same hierarchical structure with the motion detection criterion that measures the amount of motion based on the temporal co-occurrence matrices for adaptive estimation of the motion parameters. This method is fast and easily implemented using parallel processing techniques. Theoretical analysis and computer simulation show that the peak signal to noise ratio (PSNR) of the image reconstructed by the proposed method lies between those of images reconstructed by the conventional 6- and 8-parameter estimation methods with a greatly reduced computational load by a factor of about four.

• Journal of Broadcast Engineering
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• v.13 no.3
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• pp.310-318
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• 2008

The H.264/AVC standard achieves higher coding efficiency than previous video coding standards with the rate-distortion optimization (RDO) technique which selects the best coding mode and reference frame for each macroblock. As a result, the complexity of the encoder have been significantly increased. In this paper, a fast intra-mode decision algorithm is proposed to reduce the computational load of intra-mode search, which is based on the inverse tree-structure edge prediction algorithm. First, we obtained the dominant edge for each $4{\times}4$ block from local edge information, then the RDO process is only performed by the mode which corresponds to dominant edge direction. Then, for the $8{\times}8$ (or $16{\times}16$) block stage, the dominant edge is calculated from its four $4{\times}4$ (or $16{\times}16$) blocks' dominant edges without additional calculation and the RDO process is also performed by the mode which is related to dominant edge direction. Experimental results show that proposed scheme can significantly improve the speed of the intra prediction with a negligible loss in the peak signal to noise ratio (PSNR) and a little increase of bits.

• Journal of Broadcast Engineering
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• v.15 no.4
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• pp.498-512
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• 2010

Intra coding is an indispensable coding tool since it can provide random accessibility as well as error resiliency. However, it is the problem that intra coding has relatively low coding efficiency compared with inter coding in the area of video coding. Even though H.264/AVC has significantly improved the intra coding performance compared with previous video standards, H.264/AVC encoder complexity is significantly increased, which is not suitable for low bit rate interactive services. In this paper, a Voting-based Intra Mode Bit Skip (V-IMBS) scheme is proposed to improve coding efficiency as well as to reduce encoding time complexity using decoder-side prediction. In case that the decoder can determine the same prediction mode as what is chosen by the encoder, the encoder does not send that intra prediction mode; otherwise, the conventional H.264/AVC intra coding is performed. Simulation results reveal a performance increase up to 4.44% overall rate savings and 0.24 dB in peak signal-to-noise ratio while the frame encoding speed of proposed method is about 42.8% better than that of H.264/AVC.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.48 no.5
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• pp.84-95
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• 2011

In this paper, we present a deblocking algorithm that removes grid and staircase noises, which are called "blocking artifacts", occurred in the compressed satellite images. Particularly, the given satellite images are compressed with equal quantization coefficients in row according to region complexity, and more complicated regions are compressed more. However, this approach has a problem that relatively less complicated regions within the same row of complicated regions have blocking artifacts. Removing these artifacts with a general deblocking algorithm can blur complex and undesired regions as well. Additionally, the general filter lacks in preserving the curved edges. Therefore, the proposed algorithm presents an adaptive filtering scheme for removing blocking artifacts while preserving the image details including curved edges using the given quantization step size and content analysis. Particularly, WLFPCA (weighted lowpass filter using principle component analysis) is employed to reduce the artifacts around edges. Experimental results showed that the proposed method outperforms SA-DCT in terms of subjective image quality.

• Journal of Broadcast Engineering
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• v.5 no.2
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• pp.247-259
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• 2000

This paper introduces a new technique that reduces the search times and Improves the accuracy of motion estimation using high temporal and spatial correlation of motion vector. Instead of using the fixed first search Point of previously proposed search algorithms, the proposed method finds more accurate first search point as to compensating searching area using high temporal and spatial correlation of motion vector. Therefore, the main idea of proposed method is to find first search point to improve the performance of motion estimation and reduce the search times. The proposed method utilizes the direction of the same coordinate block of the previous frame compared with a block of the current frame to use temporal correlation and the direction of the adjacent blocks of the current frame to use spatial correlation. Based on these directions, we compute the first search point. We search the motion vector in the middle of computed first search point with two fixed search patterns. Using that idea, an efficient adaptive predicted direction search algorithm (APDSA) for block matching motion estimation is proposed. In the experimental results show that the PSNR values are improved up to the 3.6dB as depend on the Image sequences and advanced about 1.7dB on an average. The results of the comparison show that the performance of the proposed APDSA algorithm is better than those of other fast search algorithms whether the image sequence contains fast or slow motion, and is similar to the performance of the FS (Full Search) algorithm. Simulation results also show that the performance of the APDSA scheme gives better subjective picture quality than the other fast search algorithms and is closer to that of the FS algorithm.