• Journal of the Institute of Electronics Engineers of Korea SP
• /
• v.42 no.5 s.305
• /
• pp.29-40
• /
• 2005

According as base of digital hologram has been magnified, discussion of compression technology is expected as a international standard which defines the compression technique of 3D image and video has been progressed in form of 3DAV which is a part of MPEG. As we can identify in case of 3DAV, the coding technique has high possibility to be formed into the hybrid type which is a merged, refined, or mixid with the various previous technique. Therefore, we wish to present the relationship between various image/video coding techniques and digital hologram In this paper, we propose an efficient coding method of digital hologram using standard compression tools for video and image. At first, we convert fringe patterns into video data using a principle of CGH(Computer Generated Hologram), and then encode it. In this research, we propose a compression algorithm is made up of various method such as pre-processing for transform, local segmentation with global information of object image, frequency transform for coding, scanning to make fringe to video stream, classification of coefficients, and hybrid video coding. Finally the proposed hybrid compression algorithm is all of these methods. The tool for still image coding is JPEG2000, and the toots for video coding include various international compression algorithm such as MPEG-2, MPEG-4, and H.264 and various lossless compression algorithm. The proposed algorithm illustrated that it have better properties for reconstruction than the previous researches on far greater compression rate above from four times to eight times as much. Therefore we expect that the proposed technique for digital hologram coding is to be a good preceding research.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.23 no.6
• /
• pp.519-526
• /
• 2013

ECG signal has the feature that is repeated in a cycle of P, Q, R, S, and T waves and is sampled at a high sampling frequency in general. By using the feature of periodic ECG signals, maximizing compression efficiency while minimizing the loss of important information for diagnosis is required. However, the periodic characteristics of such amplitude and period is not constant by measuring time and patients. Even though measured at the same time, the patient's characteristics display different periodic intervals. In this paper, an adaptive multi-level coding is provided by coding adaptively the dominant and non-dominant signal interval of the ECG signal. The proposed method can maximize the compression efficiency by using a multi-level code that applies different compression ratios considering information loss associated with the dominant signal intervals and non-dominant signal intervals. For the case of long time measurement, this method has a merit of maximizing compression ratio compared with existing compression methods that do not use the periodicity of the ECG signal and for the lossless compression coding of non-dominant signal intervals, the method has an advantage that can be stored without loss of information. The effectiveness of the ECG signal compression is proved throughout the experiment on ECG signal of MIT-BIH arrhythmia database.

• Journal of the Institute of Convergence Signal Processing
• /
• v.20 no.2
• /
• pp.84-90
• /
• 2019

Tremendous computation of full search or lossless motion estimation algorithms for video coding has led development of many fast motion estimation algorithms. We still need proper control of computation and prediction quality. In the paper, we suggest an algorithm that reduces computation effectively and controls computational amount and prediction quality, while keeping prediction quality as almost the same as that of the full search. The proposed algorithm uses multiple thresholds for partial block sum and times of counting unchanged minimum position for each step. It also calculates the partial block matching error, removes impossible candidates early, implements fast motion estimation by comparing times of keeping the position of minimum error for each step, and controls prediction quality and computation easily by adjusting the thresholds. 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.