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.

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.

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.

In this paper, we propose an image compression scheme which uses a demosaicking network and a channel-wise decoder in the decoding network. For the demosaicing network, we use as the input a colored mosaiced pattern rather than the well-known Bayer pattern. The use of a colored mosaiced pattern results in the mosaiced image containing a greater amount of information pertaining to the original image. Therefore, it contributes to result in a better color reconstruction. The channel-wise decoder is composed of multiple decoders where each decoder is responsible for each channel in the color image, i.e., the R, G, and B channels. The encoder and decoder are both implemented by wavelet based auto-encoders for better performance. Experimental results verify that the separated channel-wise decoders and the colored mosaic pattern produce a better reconstructed color image than a single decoder. When combining the colored CFA with the multi-decoder, the PSNR metric exhibits an increase of over 2dB for three-times compression and approximately 0.6dB for twelve-times compression compared to the Bayer CFA with a single decoder. Therefore, the compression rate is also increased with the proposed method than with the method using a single decoder on the Bayer patterned mosaic image.

The color filter array (CFA) captures only one-third of the necessary color intensities and the full color image is generated from the captured data by interpolation. In recent years, the algorithm of Bayer patterned image compression based on “structure conversion” has achieved better image quality. On the basis of previous work, a new algorithm based on the all phase biorthogonal transform (APBT) and all phase IDCT (APIDCT) interpolation is proposed in this paper. Instead of the conventional JPEG compression, APBT is applied to the JPEG image compression (APBT-JPEG), which significantly reduces complex multiplications and makes the quantization table easier. In the interpolation step, APIDCT interpolation method is introduced. Experimental results show that the proposed algorithm outperforms the one based on “structure conversion”; and the APIDCT interpolation performs close to the conventional interpolation methods. Therefore, the algorithm proposed in this paper is more suitable for Bayer image compression.

Image compression-decompression methods have become increasingly crucial in modern times, facilitating the transfer of high-quality images while minimizing file size and internet traffic. Historically, early image compression relied on rudimentary codecs, aiming to compress and decompress data with minimal loss of image quality. Recently, a novel compression framework leveraging colorization techniques has emerged. These methods, originally developed for infusing grayscale images with color, have found application in image compression, leading to colorization-based coding. Within this framework, the encoder plays a crucial role in automatically extracting representative pixels—referred to as color seeds—and transmitting them to the decoder. The decoder, utilizing colorization methods, reconstructs color information for the remaining pixels based on the transmitted data. In this paper, we propose a novel approach to image compression, wherein we decompose the compression task into grayscale image compression and colorization tasks. Unlike conventional colorization-based coding, our method focuses on the colorization process rather than the extraction of color seeds. Moreover, we employ the Denoising Diffusion Null-Space Model (DDNM) for colorization, ensuring high-quality color restoration and contributing to superior compression rates. Experimental results demonstrate that our method achieves higher-quality decompressed images compared to standard JPEG and JPEG2000 compression schemes, particularly in high compression rate scenarios.

With the growing reliance on deep learning-based image compression techniques, ensuring the security of compressed image data has become increasingly important. Traditional encryption methods operate directly on raw image pixels, often resulting in high computational costs and inefficiencies in real-time applications. In this paper, we propose a lightweight encryption method for securing compressed images within an autoencoder-based framework. Instead of encrypting the image itself, our approach focuses on shuffling the latent tensor using a randomly generated mixing order, which is then encrypted as the key. This method significantly reduces the size of encrypted data while maintaining strong security. Our experiments, conducted on the CIFAR100 dataset, demonstrate that even a few random mixing operations make the latent tensor and the decoded image unreadable, preventing unauthorized reconstruction of the original image. Moreover, the proposed method achieves substantial computational efficiency compared to conventional encryption methods such as ChaCha20, making it particularly suitable for time-sensitive applications, including real-time drone image transmission and surveillance.

Compressed sensing is an innovative technology, which provides a new sampling mode. The key problem in compressed sensing is the construction of sensing matrix, which has an important influence on the signal sampling and reconstruction algorithm. At present, in most cases the sensing matrix is a random structure, and is difficult to realize due to its huge storage in practical applications. In this paper, we introduce a novel deterministic construction of sensing matrix via Q-matrix, which is calculated by solving the N-queens problem. The proposed sensing matrix has good orthogonality and circularity. Using the circularity of Q-matrix, we can construct sensing matrix for compressed sensing. A large number of simulation results show that the proposed sensing matrix in this paper can obtain a better quality of the reconstructed image, and it is easily realized owing to its cyclic characteristic.

JPEG images are almost always self-synchronized after an occurrence of an error in the JPEG file. In spite of the self-synchronization feature, after the synchronization point in the image, the rest of the image is not shown as it has been in the genuine image. Commonly, the blocks are shifted to right or left and usually the tinge is damaged as well. This paper suggests a way how to remedy the block shift and the tinge damage.

GPS devices typically make use of images that are excessively large to be stored as a Bit Map, so the images are always compressed. The widespread compression technique is JPEG; however JPEG has a disadvantage – JPEG assumes that the average color of beginning of each blocks line is commonly similar to the average color of end of its preceding blocks line. Almost always this assumption is wrong for GPS images. This paper proposes a method to adjust JPEG order of compression to an improved order that is more suitable for GPS images.

GPS devices typically make use of images that are excessively large to be stored as a Bit Map, so the images are always compressed. The widespread compression technique is JPEG; however JPEG has a disadvantage – JPEG assumes that the average color of beginning of each blocks line is commonly similar to the average color of end of its preceding blocks line. Almost always this assumption is wrong for GPS images. This paper proposes a method to adjust JPEG order of compression to an improved order that is more suitable for GPS images.

In this paper, it is proposed to implement the image compression, encryption and ECG data compression using by binary description of Discrete Cosine Transform (DCT), Binary Haar Transform and Discrete Hartley Transform (DHT). In this Binary Discrete Cosine Transform (Binary DCT), Binary Haar Transform and Binary Discrete Hartley Transforms (Binary DHT) are developed using the Walsh Hadamard transform (WHT). The resulting transform nearly exact the underlying transform very well, while maintaining all the advantages and properties of WHT. The Binary DCT is a well known sequency ordered Walsh Hadamard Transform (WHT), where as the Binary DHT can be considered as a new Hartley ordered WHT. Specifically, the properties of the proposed Hartley ordering are discussed and a shift copy scheme is proposed for a simple and direct generation of the Hartley ordering functions.