Earticle

Recently, the spread of video recording devices has increased, making it easy to see in our daily lives, and introducing them into fire investigations can provide information that is difficult to miss or identify in the field, so it can be useful. In the existing fire investigation technology, the main method was to analyze physical evidence such as burn patterns and debris and investigate the cause of the fire through eyewitness questions. However, It is a study on how to more accurately determine the ignition point and cause of the fire by using analysis technologies for images such as CCTV and black boxes collected at the fire site. Among the various image analysis techniques, the image compression analysis technique is a technique that can be easily applied, and as a result of experimenting and analyzing at various image reproduction speeds, it was confirmed that a certain double speed image compression helps the fire occurrence point and analysis.

Recently, the spread of video recording devices has increased, making it easy to see in our daily lives, and introducing them into fire investigations can provide information that is difficult to miss or identify in the field, so it can be useful. In the existing fire investigation technology, the main method was to analyze physical evidence such as burn patterns and debris and investigate the cause of the fire through eyewitness questions. However, It is a study on how to more accurately determine the ignition point and cause of the fire by using analysis technologies for images such as CCTV and black boxes collected at the fire site. Among the various image analysis techniques, the image compression analysis technique is a technique that can be easily applied, and as a result of experimenting and analyzing at various image reproduction speeds, it was confirmed that a certain double speed image compression helps the fire occurrence point and analysis.

A new near loss-less compression method for 2D game sprite animation image is presented to reducethe amount of the game contents resources. The sprite animation data in 2D mobile game occupies a largepart of the resource, and in particular the sprite animation data by using a lossless compression techniquecan cause a relatively very significant increase of data amount as the higher complexity of the animation. The new near-lossless method, which is an adaptive compression technique by considering the alpha valuefor transparency and the variance of color values of block, is proposed in order to obtain greatercompression efficiency without los of quality. By using experiments, we show that the proposed methodcompared to the PNG compression technique is good for compression efficiency, and in particular theeffect is very high in complex and dynamic 2D sprite animation.

Aiming at the poor compression quality of traditional image compression coding (ICC) algorithm, a multi-description ICC algorithm based on depth learning is put forward in this study. In this study, first an image compression algorithm was designed based on multi-description coding theory. Image compression samples were collected, and the measurement matrix was calculated. Then, it processed the multi-description ICC sample set by using the convolutional self-coding neural system in depth learning. Compressing the wavelet coefficients after coding and synthesizing the multi-description image band sparse matrix obtained the multi-description ICC sequence. Averaging the multi-description image coding data in accordance with the effective single point's position could finally realize the compression coding of multi-description images. According to experimental results, the designed algorithm consumes less time for image compression, and exhibits better image compression quality and better image reconstruction effect.

Since the progress of digital medical imaging techniques, it has been needed to compress the variety of medical images. In medical imaging, reversible compression of image's region of interest (ROI) which is diagnostically relevant is considered essential. Then, improving the global compression rate of the image can also be obtained by separately coding the ROI part and the remaining image (called background). For this purpose, the present work proposes an efficient reversible discrete cosine transform (RDCT) based embedded image coder designed for lossless ROI coding in very high compression ratio. Motivated by the wavelet structure of DCT, the proposed rearranged structure is well coupled with a lossless embedded zerotree wavelet coder (LEZW), while the background is highly compressed using the set partitioning in hierarchical trees (SPIHT) technique. Results coding shows that the performance of the proposed new coder is much superior to that of various state-of-art still image compression methods.

Lines and textures are natural properties of the surface of natural objects, and their images can be sparsely represented in suitable frames such as wavelets, curvelets and wave atoms. Based on characteristics that the curvelets framework is good at expressing the line feature and wavesat is good at representing texture features, we propose a model for the weighted sparsity constraints of the two frames. Furtherly, a multi-step iterative fast algorithm for solving the model is also proposed based on the split Bergman method. By introducing auxiliary variables and the Bergman distance, the original problem is transformed into an iterative solution of two simple sub-problems, which greatly reduces the computational complexity. Experiments using standard images show that the split-based Bergman iterative algorithm in hybrid domain defeats the traditional Wavelets framework or curvelets framework both in terms of timeliness and recovery accuracy, which demonstrates the validity of the model and algorithm in this paper.

Image compression is an essential technique for saving time and storage space for the gigantic amount of data generated by images. This paper introduces an adaptive source-mapping scheme that greatly improves bit-level lossless grayscale image compression. In the proposed mapping scheme, the frequency of occurrence of each symbol in the original image is computed. According to their corresponding frequencies, these symbols are sorted in descending order. Based on this order, each symbol is replaced by an 8-bit weighted fixed-length code. This replacement will generate an equivalent binary source with an increased length of successive identical symbols (0s or 1s). Different experiments using Lempel-Ziv lossless image compression algorithms have been conducted on the generated binary source. Results show that the newly proposed mapping scheme achieves some dramatic improvements in regards to compression ratios.

We propose a preprocessing method for removing floating particles from underwater images based on an analysis of the image features. We compared baseline JPEG and wavelet codec methods to determine the method best suited for underwater images. The proposed preprocessing method enhanced the compression ratio and resolution, and provided an efficient means of compressing the images. The wavelet codec method yielded better compression ratios and image resolutions. The results suggest that the wavelet codec method linked with the proposed preprocess method provides an efficient codec processor and transmission system for underwater images that are used for searches and transmitted via ultrasonic communications.

Genetic algorithm based predictor for lossless image compression is propsed. We describe a genetic algorithm to learn predictive model for lossless image compression. The error image can be further compressed using entropy coding such as Huffman coding or arithmetic coding. We show that the proposed algorithm can be feasible to lossless image compression algorithm.

프랙탈 영상압축은 블록단위의 비교방식을 사용함으로써 기존의 압축 방법에 비해 획기적으로 증대된 압축율을 보인다. 소프트웨어에 의한 실시간대 재생이 가능함에도 불구하고 프랙탈 영상압축의 실용화에 관건이 되는 것은 압축에 소요되는 시간이다. 본 논문에서는, i)영상내부의 차원정보 추출 및 군집화에 의해 유사블럭 탐색 횟수를 줄임으로써 압축에 소요되는 시간을 최소화시키는 알고리즘과 ii)영상내부의 휘도 및 명암대비 정보를 사용하여 재생영상의 화질을 증가시키는 알고리즘을 제안하고 검증함으로써, 고속이면서도 상대적으로 고화질을 유지할 수 있는 방법을 제시하였다.

Compared with previous compression methods, fractal image compression drastically increases compression rate by using block-based encoding. Although decompression can be done in real time even with softwares, the most serious problem in utilizing the fractal method is the time required for the encoding. In this paper, we propose and verify i) an algorithm that reduces the encoding time by reducing the number of similarity searching on the basis of dimensional informations, and ii) an algorithm that enhances the quality of the restored image on the basis of brightness and contrast information. Finally, a method that enables fast compression with little quality degradation is proposed.

Mixed raster content 모델 기반의 영상 부호화는 전경과 배경 레이어에 빈 영역인 don't care region (DCR)이 발생하게 되며, 이 영역에 대한 신호 채움 방식에 따라 전체적인 부호화 성능이 큰 영향을 받게 된다. DCR을 채우기 위한 대부분의 기존 기법들은 홀 주변 기존 영역의 특성을 효율적으로 이용하지 않아 신호 채움 후에도 기존 신호 영역에 존재하던 고주파 성분이 반영될 뿐 아니라, DCR 경계에서의 신호 불연속으로 인해 고주파 성분이 추가적으로 발생한다는 문제점을 갖고 있다. 이 문제를 해결하기 위해 본 논문은 우선순위 기반의 적응적 영역 확장법을 이용한 새로운 DCR 채움 알고리즘을 제안한다. 제안 알고리즘은 분수령 알고리즘을 이용하여 DCR의 각 홀 픽셀에 대한 신호 특성을 판단한 후, 이를 토대로 영역 채움의 우선순위를 결정한다. 이 우선 순위를 기반으로 영역 확장을 수행함으로써 고주파 성분을 포함하고 있는 영역의 확장이 최소화되어 전체적인 부호화 효율이 향상될 수 있다. 실험 결과를 통해 제안 알고리즘이 비교 대상 알고리즘에 비해 효율적인 신호 채움으로 우수한 부호화 성능을 가짐을 보인다.

Image coding based on mixed raster content model generates don't care regions (DCR) in foreground and background layers, and its overall coding performance is greatly affected by region filling methods for DCRs. Most conventional methods for DCR filling fail in utilizing the local signal properties in hole regions and thus the high frequency components in non-DCR regions are reflected into DCR after signal filling. In addition, further high frequency components are induced to the filled signal because of signal discontinuities in the boundary of DCR. To solve this problem, a new DCR filling algorithm using the priority-based adaptive region growing is proposed in this paper. The proposed method uses the watershed algorithm and the flooding priority of each pixel for region filling is determined from the degree of smoothness in the neighborhood area. By growing the filled region into DCR based on the computed priority, the expansion of high-textured area can be minimized which can improve the overall coding performance. Experimental results show that the proposed method outperforms conventional algorithms.

저비트율에서도 우수한 화질을 제공하는 프랙탈 영상 압축 기법들이 갖고 있는 하나의 문제점은 복호화가 반복 처리를 통해 구현되며 그 복잡도가 각각의 영상에 따라 상이하다는 것이다. 이러한 문제를 해소하기 위해 본 논문은 복호 시간을 단축시키기 위해 반복 변환이 필요 없는 프랙탈 영상 복호 알고리즘을 제안하고 그 성능을 분석함에 그 목적이 있다. 제안된 프랙탈 복호 알고리즘은 복호기에서 사용할 초기 영상과 동일한 코드북 영상을 부호기에 보유하고 있는 상태에서 부호화 과정에서는 이 코드북 영상과 부호화하려는 영상의 유사성을 찾아 프랙탈 계수를 구한다. 이후, 수신단의 복호화 과정에서는 수신된 프랙탈 계수와 기설정된 초기 영상을 이용하여 반복 변환 없이 한 번에 영상을 복호함으로써 반복 변환 횟수의 역수배만큼 복호 소요 시간을 현저하게 단축시킬 수 있었다. 제안된 방법의 타당성과 보편성을 검증하기 위해 서로 다른 분포 특성을 갖는 복수의 정지 영상과 동영상을 대상으로 복호 소요 시간과 화질 측면에서 그 성능을 평가$\cdot$분석하였다.

A problem of many fractal image compression algorithms providing good quality at low bit rate is that the decoding time rests on an iterative procedure whose complexity is imag-dependent. This paper proposes an iterative-free fractal image decoding algorithm to reduce the decoding time. In the proposed method, under the encoder previously with the same codebook image as an initial image to be used at the decoder, the fractal coefficients are obtained through calculating the similarity between the codebook image and an input image to be encoded. As the decoding time could be remarkably reduced. For verifying the validity and universality of proposed method, We evaluated and analyzed the performance of decoding time and image quality for a number of still images and a moving picture with different distributed characteristics.

본 연구는 타 병원 전원 시 발급받는 의료영상이 서로 다른 소프트웨어를 사용하는 경우 PACS상의 영상화질에 영향을 미칠 수도 있다는 점을 착안하였다. A 대학병원 영상을 DICOM 파일로 복사하여 B 대학병원 PACS상에 등록하였고 해당 대학병원에서 사용하는 소프트웨어의 압축에 따른용량과 화질을 SNR, CNR, 히스토그램을 통해 평가하였다. 압축률이 커질수록 SNR, CNR은 떨어졌고, 주목할 점은 No Compression에 비해 Lossless Compression은 용량은 1/2로 줄었지만 SNR, CNR은 변화가 없었다. 히스토그램은 압축률이 높아질수록 언더플로우 현상에 의한 정보손실이 눈에 띄게 나타났다. 타 병원 전원 시 병원마다 다른 시스템을 사용하기 때문에, 압축하여 영상을 등록하면 영상의 화질이 저하되고 정보량이 손실되므로 비압축 또는 무손실 압축방식을 사용해야 한다. 결론적으로 업로드 시 대기시간과 경제적 효율성을 고려하면, 무손실 압축방식 사용이 유용하다.

This study focused on the fact that medical images that are issued at different hospitals may affect image quality on PACS when different software is used. A university hospital image was copied to the DICOM file and registered on the PACS of the university hospital B. The capacity and image quality of the software used in the university hospital were evaluated by SNR, CNR and histogram. As the compression ratio increased, SNR and CNR tended to decrease. Note that Lossless Compression decreased the data size by half compared to No Compression, but SNR and CNR did not change. As a result of the histogram analysis, the information loss due to the underflow phenomenon was conspicuous. When moving to another hospital, No compression or lossless compression method should be used. In conclusion, it is useful to use the lossless compression method, considering waiting time and economic efficiency in uploading.

본 논문에서는 이미지 데이터를 압축하는 여러 방식 중에 YUV색 공간을 이용하여 중요도에 따라 색상 비트를 줄이는 압축 알고리즘을 제안한다. 4:2:2 서브샘플링은 영상 분야에서 표준으로 사용되고 있다. 색 정보와 사람의 망막 이 가지는 특성을 이용하여 4:2:2 서브 샘플링으로 YUV의 색상 데이터를 줄였다. YUV와 RGB는 다음과 같은 변환 행 렬을 통해 서로의 색영역으로 변환하여 사용할 수 있다. 이미지 데이터를 YUV로 색 공간으로 변환하여 상대적으로 낮 은 U , V 비트를 다운 스케일작업을 수행 한 후 수행한 후 4:2:2 서브 샘플링을 통하여 데이터를 압축한다. 기존의 방식 들과의 비교를 통하여 제안한 알고리즘의 성능을 비교하고 분석한다. 분석한 결과 중요도가 낮은 색상 요소의 정보를 줄인 결과와 원본과 비교했을 때 품질의 큰 저하 없이 이미지를 압축 할 수 있었다.

In this paper, we propose a compression algorithm that reduces color bits according to importance by using YUV color space among various methods of compressing image data. 4: 2: 2 subsampling is the standard in the field of video. By using the color information and the characteristics of the human retina, YUV color data was reduced by 4: 2: 2 subsampling. YUV and RGB can be converted to each other by using the following transformation matrix. The image data is converted into color space by YUV, and the relatively low U and V bits are subjected to the downscaling operation, and then the data is compressed through 4: 2: 2 subsampling. The performance of the proposed algorithm is compared and analyzed through comparison with existing methods. As a result of the analysis, it was possible to compress the image without reducing the information of the low importance color element and without a significant deterioration in the quality compared with the original.

임베디드 제로트리 웨이블렛(EZW : Embedded Zerotree Wavelet) 알고리즘은 원본 영상을 웨이블렛 변환하고 그 변환된 데이터를 이용하여 영상을 압축하는 기법으로써, 간단한 구조형태를 이루며 그 효율이 또한 매우 우수한 알고리즘이다. 본 논문은 이를 개선하여 압축 효율을 향상 시킨 것이다. 기본적으로 임베디드 제로트리 웨이블렛 알고리즘은 웨이블렛 변환된 데이터의 중요도를 판단하고, 그 중요도와 위치정보를 4가지로 분류하여 저장하게 된다. 이 부호는 P, N, Z, T로 나타내며 이중 P,N은 데이터의 크기와 관련한 중요도를 의미하고, 그 위치 정보를 Z, T 부호로 나타낸다. 각각의 부호들은 주부호화 과정을 통해 저장되게 되는데 이때 Z, T의 정보가 중복저장 되어 데이터양이 증가하게 된다. 본 논문에서는 중복 저장되는 데이터 량을 줄이기 위해 기존의 부호에 중복 기능을 나타내는 4가지 부호를 추가한 수정된 임베디드 제로트리 알고리즘을 제안하고, 이를 확장 임베디드 제로트리 웨이블렛(EEZW, Extended Embedded Wavelet) 알고리즘으로 명명하였다. 제안된 알고리즘은 다양한 영상을 대상으로 영상 품질을 정량적으로 표현한 PSNR(Peak Signal To Noise Rate) 수치를 비교하여 우수한 결과를 확인하였다.

EZW, also known as Embedded Zerotree Wavelet, is a technique that allows transforming original images into wavelet, then again compressing images using the transformed data. This algorithm demonstrates a simple structure and remarkable effectiveness. This paper has reformed the EZW to improve a compression efficiency.Fundamentally, EZW evaluates the priority level of wavelet-transformed data and stores them into four different categories considering the priority level of the data as well as their location information. The four categories are represented as the symbols P, N, Z, and T. Here, P and N correspond to the volume of data and the priority level whereas Z and T show the location information of data. Each letter is stored through the process of dominant pass. However, here is when the data of Z and T are stored redundantly which lead to unnecessary increase of data volume.In this paper, we propose a modified version of Embedded Zerotree Wavelet algorithm, which is designed to efficiently reduce the volume of redundantly stored data using four additionally inserted symbols. We name it EEZW, Extended Embedded Zerotree Wavelet. The proposed algorithm demonstrates the efficiency verified by a number of image and confirms an outstanding result through the PSNR(Peak Signal To Noise Rate) values, which measure their quality of images.

의료와 군사의 분야에서 원본의 이미지를 정확히 보존하면서도 압축해야 하는 필요성이 점점 커 지고 있다. 용량의 한계는 하드웨어의 발전으로 해결되고 있지만, 이미지 전송을 해야 하는 경우에는 압축을 해서 전송하는 것들이 급 변하는 세계의 변화에 필요로 대두되고 있 다. 대부분의 경우에는 HALF CONTEXT를 사용했지만 이번 논문은 FULL CONTEXT를 사용해서 이미지를 압축하려고 한다.

Purpose : In a PACS(Picture Archiving Communications System) environment, which is a very important component in a digital medical environment, the compression of digital medical images is a necessary and effective feature. In a current system where JPEG is applied to the compression of medical images, this study is to examine effectiveness and suitability when the JPEG2000, a more advanced compression algorithm for still images, is applied to the compression of medical images. In this thesis, we attempt to address the compressibility for effective clinical usage when compressing medical images, applying the objectivization of clinical evaluation as a function of compressibility. In the experimental method, the compression was applied at a fixed rate using JPEG2000, and then the result was compared with compressed images by JPEG. Method : For the performance evaluation, we choose SNR(Signal to Noise Ratio) measurement of an objective evaluation of definition and analyze a subjective evaluation by the ROCCReceiver Operating Characteristic) method, The results of the experiment showed that in the case of JPEG2000 there is hardly any distortion of images, even at high compression ratio( 100:1)，while regarding noise, the SNR remains around about 40dB. which is also relatively high. Before reading by reference to evaluative materials concerning objective compressed images, it is impossible to apply high compression to images； however, after reading, this can be applied to images that have already existed for some time.

Optimizing the utilization factor of the system resources such as efficiency, bandwidth, and the storage capacity for cost reduction is one important aim of enormous amount of studies. For the image compression one can use the embedded processors as the most suitable ones. This image compression schemes for images will be based on the Discrete Cosine Transform (DCT). This paper implements an efficient and effective algorithm for still image compression of relatively high signal to noise ratio. The implemented technique considers that only zeros of the zigzag scanning is the repeated runs. This results in possibility of zero byte of the Run Length Encoding (RLE) output elimination. Word-length reduction and higher compression ratio can be customized.