• The Journal of Korean Institute of Communications and Information Sciences
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• v.28 no.2C
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• pp.121-128
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• 2003

In this paper, we propose a new disparity estimation method called object based block matching algorithm(OBMA) for stereoscopic images which is able to reduce the blocking artifact. In the proposed algorithm, edge information of the given image is first extracted and then we estimate the disparity of each segmented object to remove the blocking artifact. In the experimental results, it is proven that the proposed algorithm has about the same performance as the old BMA algorithm while it achieves much better subjective quality.

• Journal of information and communication convergence engineering
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• v.9 no.6
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• pp.671-675
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• 2011

DVC (Distributed Video Coding) and FRUC (Frame Rate Up Conversion) techniques need to have an efficient motion compensated frame interpolation algorithms. Conventional works of these applications have mainly focused on the performance improvement of overall system. But, in some applications, it is necessary to evaluate how well the MCI (Motion Compensated Interpolation) frame matches the original frame. For this aim, this paper deals with the modeling methods for evaluating the block-based matching cost. First, several matching criteria, which have already been dealt with the motion compensated frame interpolation, are introduced and then combined to make estimate models for the size of MSE (Mean Square Error) noise of the MCI frame to original one. Through computer simulations, it is shown that the block-based matching criteria are evaluated and the proposed model can be effectively used for estimating the MSE noise.

• Journal of the Korea Society of Computer and Information
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• v.7 no.4
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• pp.102-107
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• 2002

Compression coding based on block coding has been applied in image and video compression standard. But there is no block effect measurement due to block based image coding. In this paper, we propose a objective block effects measurement to reconstructed image using subblock DCT coding. Experimental results show that the block effects measures given by the suggested method agree well with the subjective ranking. This new objective measurement is simple and effective in measuring the block effect in the reconstructed image.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.24 no.12B
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• pp.2438-2446
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• 1999

Most of block based stereo pair compression schemes utilize the constant block size and searching area size for all the stereo pairs to find the best matching block. However, it is not efficient to use the same block size and size of searching area to all the stereo pairs, since each stereo pair has different disparity. In this Paper, we propose a method to calculate the total disparity of stereo pairs, and show how to determine the size of the block and searching area which are applied for the block based compression of the stereo pairs.

• Journal of the Korea Society of Computer and Information
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• v.22 no.5
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• pp.1-9
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• 2017

Recently, flash memory has been in a great demand from embedded system sectors for storage devices. However, program/erase (P/E) cycles per block are limited on flash memory. For the limited number of P/E cycles, many wear leveling techniques are studied. They prolonged the life time of flash memory using information tables. As one of the techniques, block erase table (BET) method using a bit array table was studied for embedded devices. However, it has a disadvantage in that performance of wear leveling is sharply low, when the consumption of memory is reduced. To solve this problem, we propose a novel wear leveling technique using Sampling-based Block Erase Table (SBET). SBET relates one bit of the bit array table to each block by using exclusive OR operation with round robin function. Accordingly, SBET enhances accuracy of cold block information and can prevent to decrease the performance of wear leveling. In our experiment, SBET prolongs life time of flash memory by up to 88%, compared with previous techniques which use a bit array table.

• IEIE Transactions on Smart Processing and Computing
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• v.5 no.2
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• pp.129-142
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• 2016

In Wyner-Ziv coding, video signals are reconstructed by correcting side information generated by block-based motion estimation/compensation at the decoder. The correction is not always accurate due to the limited number of parity bits and early stopping of low-density parity check accumulate (LDPCA) decoding in distributed video coding, or due to the limited number of measurements in distributed compressive video sensing. The blocking artifacts caused by block-based processing are usually conspicuous in smooth areas and degrade the perceptual quality of the reconstructed video. Conventional deblocking filters try to remove the artifacts by treating both sides of the block boundary equally; however, coding errors generated by block-based processing are not necessarily the same on both sides of the block boundaries. Such a block-wise difference is exploited in this paper to improve deblocking for Wyner-Ziv frameworks by designing a filter where the deblocking strength at each block can be non-identical, depending on the reliability of the reconstructed pixels. Test results show that the proposed filter not only improves subjective quality by reducing the coding artifacts considerably, but also gains rate distortion performance.

• Journal of Information Processing Systems
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• v.15 no.2
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• pp.410-421
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• 2019

Distributed compressed sensing (DCS) states that we can recover the sparse signals from very few linear measurements. Various studies about DCS have been carried out recently. In many practical applications, there is no prior information except for standard sparsity on signals. The typical example is the sparse signals have block-sparse structures whose non-zero coefficients occurring in clusters, while the cluster pattern is usually unavailable as the prior information. To discuss this issue, a new algorithm, called backtracking-based adaptive orthogonal matching pursuit for block distributed compressed sensing (DCSBBAOMP), is proposed. In contrast to existing block methods which consider the single-channel signal reconstruction, the DCSBBAOMP resorts to the multi-channel signals reconstruction. Moreover, this algorithm is an iterative approach, which consists of forward selection and backward removal stages in each iteration. An advantage of this method is that perfect reconstruction performance can be achieved without prior information on the block-sparsity structure. Numerical experiments are provided to illustrate the desirable performance of the proposed method.

• Journal of Broadcast Engineering
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• v.20 no.3
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• pp.398-407
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• 2015

Compressive sensing (CS) has drawn much interest as a novel sampling technique that enables sparse signal to be sampled under the Nyquitst/Shannon rate. By noting that the block-based CS can still keep spatial correlation in measurement domain, this paper proposes to adapt sampling rate of each block in frame according to its characteristic defined by edge information. Specifically, those blocks containing more edges are assigned more measurements utilizing block-wise correlation in measurement domain without knowledge about full sampling frame. For natural image, the proposed adaptive rate allocation shows considerable improvement compared with fixed subrate block-based CS in both terms of objective (up to 3.29 dB gain) and subjective qualities.

• Journal of Information Processing Systems
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• v.13 no.2
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• pp.360-369
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• 2017

In this paper, a new iterative algorithm for reconstructing block sparse signals, called block backtracking-based adaptive orthogonal matching pursuit (BBAOMP) method, is proposed. Compared with existing methods, the BBAOMP method can bring some flexibility between computational complexity and reconstruction property by using the backtracking step. Another outstanding advantage of BBAOMP algorithm is that it can be done without another information of signal sparsity. Several experiments illustrate that the BBAOMP algorithm occupies certain superiority in terms of probability of exact reconstruction and running time.

• Journal of Korea Society of Digital Industry and Information Management
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• v.8 no.4
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• pp.13-18
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• 2012

In this paper, we propose a noise estimation method for noise reduction. It is based on block and pixel-based noise estimation. We assume that an input image is contaminated by the additive white Gaussian noise. Thus, we use an adaptive Gaussian filter and estimate the amount of noise. It computes the standard deviation of each block and estimation is performed on pixel-based operation. The proposed algorithm divides an input image into blocks. This method calculates the standard deviation of each block and finds the minimum standard deviation block. The block in flat region shows well noise and filtering effects. Blocks which have similar standard deviation are selected as test blocks. These pixels are filtered by adaptive Gaussian filtering. Then, the amount of noise is calculated by the standard deviation of the differences between noisy and filtered blocks. Experimental results show that our proposed estimation method has better results than those by existing estimation methods.