• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2017.10a
• /
• pp.159-161
• /
• 2017

In this paper, we propose an adaptive search range allocation algorithm for high-performance HEVC encoder and a hardware architecture suitable for the proposed algorithm. In order to improve the prediction performance, the existing motion vector is configured with the motion vectors of the neighboring blocks as prediction vector candidates, and a search range of a predetermined size is allocated using one motion vector having a minimum difference from the current motion vector. The proposed algorithm reduces the computation time by reducing the size of the search range by assigning the size of the search range to the rectangle and octagon type according to the structure of the motion vectors for the surrounding four blocks. Moreover, by using all four motion vectors, it is possible to predict more precisely. By realizing it in a form suitable for hardware, hardware area and computation time are effectively reduced.

• The Journal of the Korea Contents Association
• /
• v.18 no.12
• /
• pp.35-45
• /
• 2018

Motion Estimation(ME) has been developed to reduce the redundant data in digital video signal. ME is an important part of video encoding system, However, it requires huge computational complexity of the encoder part, and fast motion search methods have been proposed to reduce huge complexity. Multi- view video is obtained by capturing on a three-dimensional scene with many cameras at different positions and its complexity increases in proportion to the number of cameras. In this paper, we proposed an efficient motion method which chooses a search pattern adaptively by using the temporal-spatial correlation of the block and the characteristics of the block. Experiment results show that the computational complexity reduction of the proposed method over TZ search method and FS method can be up to 70~75% and 99% respectively while keeping similar image quality and bit rates.

• Journal of Broadcast Engineering
• /
• v.27 no.3
• /
• pp.332-340
• /
• 2022

Recently, with the development of deep learning and artificial neural network technologies, research on the application of neural network has been actively conducted in the field of video coding. In particular, deep learning-based intra prediction is being studied as a way to overcome the performance limitations of the existing intra prediction techniques. This paper presents a method of context-adaptive neural network-based intra prediction model training and its coding performance analysis. In other words, in this paper, we implement and train a known intra prediction model based on convolutional neural network (CNN) that predicts a current block using contextual information from reference blocks. Then, we integrate the trained model into HM16.19 as an additional intra prediction mode and evaluate the coding performance of the trained model. Experimental results show that the trained model gives 0.28% BD-rate bit saving over HEVC in All Intra (AI) coding mode. In addition, the coding performance change of training considering block partition is also presented.

• Journal of Broadcast Engineering
• /
• v.17 no.2
• /
• pp.316-324
• /
• 2012

In this paper, a novel descreening method using HOG(histogram of gradient)-based adaptive smoothing filter is proposed. Conventional edge-oriented smoothing methods does not provide enough smoothing to the halftone image due to the edge-like characteristic of the halftone noise. Moreover, clustered-dot halftoning method, which is commonly used in printing tends to create Moire pattern because of the intereference in color channels. Therefore, the proposed method uses HOG to distinguish edges and the amount of smoothing to be performed on the halftone image is then calculated according to the magnitude of the HOG in the edge and edge normal orientation. The proposed method was tested on various scanned halftone materials, and the results show that it effectively removes halftone noises as well as Moire pattern while preserving image details.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.35 no.5C
• /
• pp.464-471
• /
• 2010

The demand for high quality of multimedia applications, which far surpasses the rapid evolution of transmission and storage technologies, makes better compression coding capabilities ever increasingly more important. In order to provide enhanced video coding performance, this paper proposes an efficient competition-based motion vector coding scheme. The proposed algorithm adaptively forms the motion vector predictors based on the contexts of scene characteristics such as camera motion and nearby motion vectors, providing more efficient candidate predictors than the previous competition-based motion vector coding schemes which resort to the fixed candidates optimized by extensive simulations. Up to 200% of compression gain was observed in the experimental results for the proposed scheme applied to the motion vector selection for skip mode processing.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.29 no.9A
• /
• pp.1013-1021
• /
• 2004

In the WCDMA mobile multimedia communication system, the adaptive array antenna IS adopted to improve the performance of the system by reducing inter-user interference using antenna beam control. Usually, the interference resulted from the higher data rate users is much more significant to the lower data rate users than the other way around, so the overall performance can be enhanced by reducing the interference from higher data rate users. In order to maximize the efficiency of adaptive antenna operation, an optimal call admission control, especially during handoff, adaptive to the data rates is a critical problem. In this paper, We propose a call admission control algorithm based on the Soft QoS concept for the efficient processing of the handoff of higher data rate calls, and an adaptive handoff control mechanism according to the data rates. The proposed algorithm has been evaluated by computer simulation that it accommodates high data rate users among many lower data rate users much better, and the average call blocking probability for lower rate users becomes much lower than the conventional call admission control algorithm.

• Journal of Korea Water Resources Association
• /
• v.46 no.10
• /
• pp.969-976
• /
• 2013

Leakage in water distribution system causes social and economic losses by direct water loss into the ground, and additional energy demand for water supply. This research suggests a leak detection model of using adaptive Kalman filtering on real-time data of pipe flow. The proposed model takes into account hourly and daily variations of water demand. In addition, the model's prediction accuracy is improved by automatically calibrating the covariance of noise through innovation sequence. The adaptive Kalman filtering shows more accurate result than the existing Kalman method for virtual sine flow data. Then, the model is applied to data from two real district metered area in JE city. It is expected that the proposed model can be an effective tool for operating water supply system through detecting burst leakage and abnormal water usage.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.19 no.5
• /
• pp.47-54
• /
• 2019

In this paper, we consider a constant modulus (CM) based blind adaptive receiver design for downlink multi-carrier code-division multiple access (MC-CDMA) systems employing simple space-time block coding (STBC). In the paper, filter weight vectors used for the detection of the transmitted symbols are partitioned into its subvectors and then, special relations among the optimal subvectors minimizing the CM metric are derived. Using the special relations, we present a modified CM metric and propose a new blind adaptive stochastic-gradient CM algorithm (SG-CMA) by minimizing the modified CM metric. The proposed blind adaptive SG-CMA has faster convergence rate than the conventional SG-CMA because the filter weight vectors of the proposed scheme are updated in the region of satisfying the derived special relations. Computer simulation results are given to verify the superiority of the proposed SG-CMA.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.19 no.11
• /
• pp.2264-2275
• /
• 1994

In this paper, image compression based on neural networks is presented with block classification and coding. Multilayer neural networks with error back-propagation learning algorithm are used to transform the normalized image date into the compressed hidden values by reducing spatial redundancies. Image compression can basically be achieved with smaller number of hidden neurons than the numbers of input and output neurons. Additionally, the image blocks can be grouped for adaptive compression rates depending on the characteristics of the complexity of the blocks in accordance with the sensitivity of the human visual system(HVS). The quantized output of the hidden neuron can also be entropy coded for an efficient transmission. In computer simulation, this approach lie in the good performances even with images outside the training set and about 25:1 compression rate was achieved using the entropy coding without much degradation of the reconstructed images.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.24 no.9A
• /
• pp.1411-1418
• /
• 1999

In this paper, we propose a fast motion estimation algorithm using motion prediction and neural network. Considering that the motion vectors have high spatial correlation, the motion vector of current block is predicted by those of neighboring blocks. The codebook of motion vector is designed by Kohonen self-organizing feature map(KSFM) learning algorithm which has a fast learning speed and 2-D adaptive chararteristics. Since the similar codevectors are closely located in the 2-D codebook the motion is progressively estimated from the predicted codevector in the codebook. Computer simulation results show that the proposed method has a good performance with reduced computational complexity.