• The Transactions of the Korean Institute of Electrical Engineers D
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• v.53 no.6
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• pp.453-456
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• 2004

Due to the bandwidth and storage limitations medical images are needed to be compressed before transmission and storage. DICOM (Digital Imaging and Communications in Medicine) specification, which is the medical images standard, provides a mechanism for supporting the use of JPEG still image compression standard. In this paper, we explain a method for compressing medical images by PEG standard and propose two methods for JPEG compression. First, because medical images differ from natural images in optical feature, we propose a method to design adaptively the quantization table using spectrum analysis. Second, because medical images have higher pixel depth than natural images do, we propose a method to design Huffman table which considers the probability distribution feature of symbols. Simulation results show the improved performance compared to the quantization table and the adjusted Huffman table of JPEG standard.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.22 no.5
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• pp.915-927
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• 1997

In this paper, we propose an error concealment technique using directional interpolation in block-based image compression. In the proposed method the edge direction is determined by finding the maximum correlation coefficients of boundary pixels of blocks neighboring the errored block in spatial domain. Then the errored block is interpolated linearly or bilinearly along the determined edge direction. The proposed method can conceal the block error, the macro block error, and the slice error adaptively. Also, the parameters for the directional interpolation are represented by closed forms. When applied to compressed images, the proposed method shows superior subjective and objective quality to conventional error concealment methods.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.10
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• pp.4160-4176
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• 2015

Data compression like image and video compression has come a long way since the introduction of Compressive Sensing (CS) which compresses sparse signals such as images, videos etc. to very few samples i.e. M < N measurements. At the receiver end, a robust and efficient recovery algorithm estimates the original image or video. Many prominent algorithms solve least squares problem (LSP) iteratively in order to reconstruct the signal hence consuming more processing time. In this paper non-iterative threshold based recovery algorithm (NITRA) is proposed for the recovery of images and videos without solving LSP, claiming reduced complexity and better reconstruction quality. The elapsed time for images and videos using NITRA is in ㎲ range which is 100 times less than other existing algorithms. The peak signal to noise ratio (PSNR) is above 30 dB, structural similarity (SSIM) and structural content (SC) are of 99%.

• Journal of the Korean Institute of Telematics and Electronics
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• v.23 no.5
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• pp.732-736
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• 1986

This paper propose a novel vector-quantized block truncation coder for image data compression. A data compression ratio of about 3-6 times larger than that of the BTC can be achieved by utilizign a vector quantizer with the BTC. A vector quantizer was realized by computer simulation. The compressed data rate of 0.7~1.0 bit/pel with intraframe coder and that of 0.3~0.5 bit/pel with interframe coder gives a good performance.

• Journal of the Korean Society for Precision Engineering
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• v.10 no.3
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• pp.220-230
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• 1993

Photoelastic isochromatic fringes related to the difference of principal stresses have some bandwidth whose light intensities are not constant and asymmetrical in experimental images. Hence, it is difficult to measure fringe order accurately at a data point by visual observation. In this study, a method of fringe sharpening, which can extract shapened lines from both full-and half-order fringes by digital image processing, is developed. To test the method, various simple photelastic fringe patterns are simulated and their images are processed to yield sharpened lines. The method is then applied to general problems such as images of a circular disk compressed by diametrically concentrated loads and a circular cylinder sybject to internal pressure. The procedure is proved to be capable of extracting sharpened lines accurately from photoelastic isochromatic fringes.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.4
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• pp.1457-1470
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• 2015

In wireless multimedia sensor networks (WMSN), the latency of transmission is an increasingly problem. With the improvement of resolution, the time cost in image and video compression is more and more, which seriously affects the real-time of WMSN. In JPEG system, the core of the system is DCT, but DCT-JPEG is not the best choice. Block-based DCT transform coding has serious blocking artifacts when the image is highly compressed at low bit rates. APBT is used in this paper to solve that problem, but APBT does not have a fast algorithm. In this paper, we analyze the structure in JPEG and propose a parallel framework to speed up the algorithm of JPEG on GPU. And we use all phase biorthogonal transform (APBT) to replace the discrete cosine transform (DCT) for the better performance of reconstructed image. Therefore, parallel APBT-JPEG is proposed to solve the real-time of WMSN and the blocking artifacts in DCT-JPEG in this paper. We use the CUDA toolkit based on GPU which is released by NVIDIA to design the parallel algorithm of APBT-JPEG. Experimental results show that the maximum speedup ratio of parallel algorithm of APBT-JPEG can reach more than 100 times with a very low version GPU, compared with conventional serial APBT-JPEG. And the reconstructed image using the proposed algorithm has better performance than the DCT-JPEG in terms of objective quality and subjective effect. The proposed parallel algorithm based on GPU of APBT also can be used in image compression, video compression, the edge detection and some other fields of image processing.

• Journal of Broadcast Engineering
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• v.25 no.2
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• pp.200-207
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• 2020

Recently, deep neural networks such as CNN are showing excellent performance in various fields such as image classification, object recognition, visual quality enhancement, etc. However, as the model size and computational complexity of deep learning models for most applications increases, it is hard to apply neural networks to IoT and mobile environments. Therefore, neural network compression algorithms for reducing the model size while keeping the performance have been being studied. In this paper, we apply few compression methods to CNN models and evaluate their performances in the embedded environment. For evaluate the performance, the classification performance and inference time of the original CNN models and the compressed CNN models on the image inputted by the camera are evaluated in the embedded board equipped with QCS605, which is a customized AI chip. In this paper, a few CNN models of MobileNetV2, ResNet50, and VGG-16 are compressed by applying the methods of pruning and matrix decomposition. The experimental results show that the compressed models give not only the model size reduction of 1.3~11.2 times at a classification performance loss of less than 2% compared to the original model, but also the inference time reduction of 1.2~2.21 times, and the memory reduction of 1.2~3.8 times in the embedded board.

• Journal of Biomedical Engineering Research
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• v.23 no.1
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• pp.61-72
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• 2002

In this parer, a new harmonic imaging technique is Proposed and evaluated experimentally. In the Proposed method. a weighted chirp signal with a hanning window is transmitted. The RF samples obtained on each array element are individually compressed by correlating with the reference signal defined as the 2nd harmonic component($2f_0$) of a transmitted chirp signal generated in a square-law system. The correlator output will then consist of the compressed version of the $2f_0$ component generated in tissue and the crosscorrelation sequence of the fundamental($f_0$) and 2f$_{0}$components. The Proposed method uses the compressed $2f_0$ component to form an image. for which the crosscorrelation term should be suppressed below at least -50dB. The Proposed method has two process, 2f$_{0}$-correlation and $2f_0$-correlation(PI) . $2f_0$-correlation can successfully eliminate the $f_0$ component with a single transmit-receive events and therefore is more efficient than the conventional Pulse inversion method in terms of the frame rate. 2i)-correlation(Pl) Performs pulse compression after applying pulse inversion method for the 2nd harmonic image with high resolution and SNR. Another advantage of the proposed method is that the SNR of 2nd harmonic imaging can be improved without limitation by increasing the duration of the chirp signal. The proposed method was verified through both the computer simulations and actual experiments .ts .

• Journal of the Korea Computer Graphics Society
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• v.8 no.3
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• pp.9-16
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• 2002

Recently, high dynamic range (HDR) compression has attracted much attention due to the wide availability of HDR images. In this paper, we present an HDR compression method using a progressive image, which is a multi-level image representation based on a progressive mesh. An HDR image can be decomposed into a base image and a sequence of details by conversion into a progressive image. This decomposition provides a good structure to highly compress the dynamic range while preserving image details. The base image and larger details are considerably scaled down but smaller details are slightly scaled down. Experimental results show that our method successfully generates HDR compressed images without halo artifacts by controlling two intuitive parameters.

• The Journal of the Korea Contents Association
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• v.9 no.5
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• pp.13-23
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• 2009

One of the main purpose of multiple-view image processing technology is support realistic 3D image to device user by using multiple viewpoint display devices and compressed data restoration devices. This paper proposes a multiple view image synthesis scheme based on RAM disk which makes possible to browse 3D images generated by applying effective composing method to real time input stereo images. The proposed scheme first converts input images to binary image. We applies edge detection algorithm such as Sobel algorithm and Prewiit algorithm to find edges used to evaluate disparities from images of 4 multi-cameras. In addition, we make use of time interval between hardware trigger and software trigger to solve the synchronization problem which has stated ambiguously in related studies. We use a unique identifier on each snapshot of images for distributed environment. With respect of performance results, the proposed scheme takes 0.67 sec in each binary array. to transfer entire images which contains left and right side with disparity information for high quality 3D image browsing. We conclude that the proposed scheme is suitable for real time 3D applications.