• Journal of the Institute of Convergence Signal Processing
• /
• v.6 no.4
• /
• pp.176-181
• /
• 2005

This paper presents an equal bit rate control algorithm utilizing the statistical change between the previous frame and the current frame. The previous studies on the model-based rate control have focused on the models of bit rate and distortion in types of coders, in terms of the quantization parameter. The proposed algorithm improves the typical model-based rate control by updating a model parameter instead of modeling a better model of the rate and distortion. The proposed algorithm updates this model parameter by recognizing the change in statistics between the previous frame and the current frame. We implement the proposed algorithm in MPEG-4 coders and verify its performance while comparing it to the TMN8's approach (up to 0.6dB of improvement).

• Proceedings of the Korea Institute of Convergence Signal Processing
• /
• 2000.12a
• /
• pp.13-16
• /
• 2000

In the recent mobile communication systems, the performance of Turbo Code using the error correction coding depends on the interleaver influencing the free distance determination and the recursive decoding algorithms that is executed in the turbo decoder. However, performance depends on the interleaver depth that need great many delay over the reception process. Moreover, Turbo Code has been known as the robust coding methods with the confidence over the fading channel. The International Telecommunication Union(ITU) has recently adopted as the standardization of the channel coding over the third generation mobile communications the same as IMT-2000. Therefore, in this paper, we proposed of that has the better performance than existing Turbo Decoder that has the parallel concatenated four-step structure using MAP algorithm. In the real-time voice and video service over the third generation mobile communications, the performance of the proposed method was analyzed by the reduced decoding delay using the variable decoding method by computer simulation over AWGN and lading channels.

• Journal of the Institute of Convergence Signal Processing
• /
• v.20 no.3
• /
• pp.171-176
• /
• 2019

Drones are highly utilized because they can efficiently acquire long-distance videos. In drone operation, the speed, which is the magnitude of the velocity, can be set, but the moving direction cannot be set, so accurate information about the drone's movement should be estimated. In this paper, we estimate the velocity of the drone moving at a constant speed and direction. In order to estimate the drone's velocity, the displacement of the target frame to minimize the sum of absolute difference (SAD) of the reference frame and the target frame is obtained. The ground truth of the drone's velocity is calculated using the position of a certain matching point over all frames. In the experiments, a video was obtained from the drone moving at a constant speed at a height of 150 meters. The root mean squared error (RMSE) of the estimated velocities in x and y directions and the RMSE of the speed were obtained showing the reliability of the proposed method.

• Journal of the Institute of Convergence Signal Processing
• /
• v.20 no.3
• /
• pp.159-164
• /
• 2019

Detecting wildfire using only color in image information is a very difficult issue. This paper proposes an algorithm to detect forest fire area by analyzing color and motion of the area in the video including forest fire. The proposed algorithm removes the background region using the Gaussian Mixture based background segmentation algorithm, which does not depend on the lighting conditions. In addition, the RGB channel is changed to an HSV channel to extract flame candidates based on color. The extracted flame candidates judge that it is not a flame if the area moves while labeling and tracking. If the flame candidate areas extracted in this way are in the same position for more than 2 minutes, it is regarded as flame. Experimental results using the implemented algorithm confirmed the validity.

• Journal of the Institute of Convergence Signal Processing
• /
• v.16 no.4
• /
• pp.129-133
• /
• 2015

In this paper, we propose a fast motion estimation algorithm which is important in the performance of video encoding. Conventional fast motion estimation algorithms have serious problems of low prediction quality in some frames and still much computation. In the paper, we propose an algorithm that reduces unnecessary computations only, while keeping prediction quality almost similar to that of the full search. The proposed algorithm uses distribution of probability of motion vectors, divides search range into several groups according to its importance, and applies adaptive block matching criteria for each group of search range. The proposed algorithm takes only 3~5% in computational amount and has decreased prediction quality about 0~0.01dB compared with the fast full search algorithm.

• Journal of the Institute of Convergence Signal Processing
• /
• v.19 no.4
• /
• pp.192-198
• /
• 2018

Recently, the use of drone has been increasing rapidly in many ways. A drone can capture remote objects efficiently so it is suitable for surveillance and security systems. This paper discusses three methods for detecting moving vehicles using a drone. We compare three target detection methods using a background frame, preceding frames, or moving average frames. They are subtracted from a current frame. After the frame subtraction, morphological filters are applied to increase the detection rate and reduce the false alarm rate. In addition, the false alarm region is removed based on the true size of targets. In the experiments, three moving vehicles were captured by a drone, and the detection rate and the false alarm rate were obtained by three different methods and the results are compared.

• Journal of the Institute of Convergence Signal Processing
• /
• v.19 no.4
• /
• pp.199-205
• /
• 2018

So many fast motion estimation algorithms for prediction quality and computational reduction have been published due to tremendous computations of full search algorithm. In the paper, we suggest an algorithm that reduces computation effectively, while keeping prediction quality as almost same as that of the full search. The proposed algorithm based on multiple threshold filter calculates the sum of partial block matching error for each candidate, selects the candidates for the next step, compares the stability of optimal candidates with minimum error, removes impossible candidates, and calculates optimal motion vectors by determining the progress of the next step. By doing that, we can find the minimum error point as soon as possible and obtain the better performance of calculation speed by reducing unnecessary computations. The proposed algorithm can be combined with conventional fast motion estimation algorithms as well as by itself, further reduce computation while keeping the prediction quality as almost same as the algorithms, and prove it in the experimental results.

• Journal of the Institute of Convergence Signal Processing
• /
• v.21 no.2
• /
• pp.55-60
• /
• 2020

In this paper, we propose a fast block matching algorithm of motion estimation using early detection of optimal candidate with high priority and a threshold. Even though so many fast algorithms for motion estimation have been published to reduce computational reduction full search algorithm, still so many works to improve performance of motion estimation are being reported. The proposed algorithm calculates block matching error for each candidate with high priority from previous partial matching error. The proposed algorithm can be applied additionally to most of conventional fast block matching algorithms for more speed up. By doing that, we can find the minimum error point early and get speed up by reducing unnecessary computations of impossible candidates. The proposed algorithm uses smaller computation than conventional fast full search algorithms with the same prediction quality as the full search algorithm. Experimental results shows that the proposed algorithm reduces 30~70% compared with the computation of the PDE and full search algorithms without any degradation of prediction quality and further reduces it with other fast lossy algorithms.

• Journal of the Institute of Convergence Signal Processing
• /
• v.22 no.2
• /
• pp.57-63
• /
• 2021

The purpose of this study is to develop a Smart Helmet to safeguard forest firefighting crews and provide on-site information in real time. The Smart Helmet for forest firefingting crews is equipped with a camera, video/voice communication module, GPS, Bluetooth, and LTE module to promote the safety of them, and through the Smart Helmet, the site situation is is transmitted in real time, and full duplex communication is possible. As a result of testing using the Smart Helmet, the control center was able to receive on-site information and communication with on-site forest firefighting crews. Through site evaluation and user evaluation, it was confirmed that the Smart Helmet needs to be improved. The developed Smart Helmet can be used in various ways in forest disasters and forest industry.

• Journal of the Institute of Convergence Signal Processing
• /
• v.23 no.1
• /
• pp.44-49
• /
• 2022

Abstract Recently, with the development of IoT technology and communication media, various video equipment is being used in industrial fields. Image data acquired from cameras and sensors are easily affected by noise during transmission and reception, and noise removal is essential as it greatly affects system reliability. In this paper, we propose a weight filter algorithm based on the pixel value distribution pattern to preserve details in the process of restoring images damaged in AWGN. The proposed algorithm calculates weights according to the pixel value distribution pattern of the image and restores the image by applying a filtering mask. In order to analyze the noise removal performance of the proposed algorithm, it was simulated using enlarged image and PSNR compared to the existing method. The proposed algorithm preserves important characteristics of the image and shows the performance of efficiently removing noise compared to the existing method.