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Forgetting factor recursive least squares (FFRLS) is an effective lossless compression technique for hyperspectral images. However, the forgetting factor of the FFRLS algorithm is a predetermined fixed value that cannot be adjusted in real time, which can affect prediction accuracy. To address this problem, a new lossless compression method for hyperspectral images using variable forgetting factor recursive least squares was developed. The impact of the forgetting factor on the FFRLS algorithm was analyzed, and a forgetting factor adjustment function was constructed using the average of the posterior prediction residuals in a causal neighborhood as a variable to adjust the forgetting factor dynamically. The performance of this algorithm was verified using NASA's AIRS and CCSDS's 2006 AVIRIS images with minimum average bit rates of 3.66 and 4.07 bits per pixel, respectively. The experimental results show that the proposed algorithm improves prediction accuracy compared with the algorithm with a fixed forgetting factor and achieves better compression performance.

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.

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.

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 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.

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.

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.

Images include information about human body which is used for different purpose such as medical examination security and other plans Compression of images is used in some applications such as profiling information and transmission systems. Regard to importance of images information, lossless or loss compression is preferred. Lossless compressions are JPEG, JPEG-LS and JPEG2000 are few well-known methods for lossless compression. We will use differential pulse code modulation for image compression with Huffman encoder, which is one of the latest and provides good compression ratio, peak signal to noise ratio and minimum mean square error. In real time application which needs hardware implementation, low complex algorithm accelerate compression process. In this paper, we use differential pulse code modulation for image compression lossless and near-lossless compression method is introduced which is efficient due to its high compression ratio and simplicity. This method is consists of a new transformation method called Enhanced DPCM Transformation (EDT) which has a good energy compaction and a suitable Huffman encoding. After introducing this compression method it is applied on different images from Corel dataset for experimental results and analysis. Also we compare it with other existing methods with respect to parameter compression ratio, peak signal noise ratio and mean square error.

Images include information about human body which is used for different purposes such as medical, security and other plans. Compression of images is used in some applications such as profiling data and transmission systems. Regard to importance of images information, lossless or lossy compression is preferred. Lossless compressions are JPEG, JPEG-LS and JPEG 2000 is few well-known methods for lossless compression. We will use differential pulse code modulation for image compression with Huffman encoder, which is one of the latest and provides good compression ratio, peak signal noise ratio and minimum mean square error. In real time application which needs hardware implementation, low complex algorithm accelerates compression process. In this dissertation, we use differential pulse code modulation for image compression lossless and near-lossless compression method is introduced which is efficient due to its high compression ratio and simplicity. This method is consists of a new transformation method called Enhanced DPCM Transformation (EDT) which has a good energy compaction and a suitable Huffman encoding. After introduce this compression method, it is apply on different images from Corel dataset for experimental results and analysis. As well we compare it with other existing methods with respect to parameter compression ratio, peak signal noise ratio and mean square error.

Ultra high definition (UHD) game scenes have caused the memory bandwidth problem. The lossless DPCM-GR based compression algorithm [12] using NVIDIA CUDA(Compute Unified Device Architecture) like general purpose GPU (GPGPU) computing relieves the bandwidth problem without sacrificing image quality, which supports bit parallel pipelining. This paper increases the memory bandwidth efficiency using the shared memory of CUDA based on the compression algorithm [12]. Also, various asynchronous transfer configurations which can overlap the kernel execution and data transfer between the Host and the CUDA device are implemented with the page-locked host memory. Experimental results show that GPGPU CUDA computing obtains the maximum 87.5 and 30.6 times speedups for GTX650Ti and GT330, respectively, comparing to Host CPU. Also, the maximum reductions of the compression time for GTX650Ti and GT330 are 54.1% and 30.3%, respectively, among various concurrency transfer configurations.

We propose a new lossless compression algorithm for hyper spectral images based on the third -order interband predictor and the backward pixel search scheme (IP3-BPS). Specifically, we propose an adaptive search threshold algorithm and a bi-directional pixel search scheme. The resulting algorithm takes the bi-directional pixel search and the backward pixel search with adaptive search threshold as the last two predictors; its performance evaluation on Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) 1997 images shows that it outperforms the original IP3-BPS algorithm, at lower computational complexity level.

A new binary (bit-level) lossless image compression method based on a well-known error correcting BCH codes has been introduced in this paper. The BCH encoder converts the message of k bits to a codeword of length n by adding 3 parity bits. In contrast the decoder eliminates these parity bits after verifying the received message; therefore, the proposed method utilizes this idea by dividing the image into blocks of size 7 bits. These blocks entered to the BCH decoder who eliminates the parity bits. This reduces the block size to 4 bits. The output will be in two folds first the compressed image file and the second a file contains the keys. Each block is tested to find if it is a valid block or not (valid / non-valid codeword). In order to distinguish between them during the decompression process, the proposed method adds 1 for the valid codeword and 0 for the invalid codeword and saved in another file to be the key that used in the decoding stage. We then implement the Huffman codes on the compressed file to increase the compression ratio. On the other hand, two different algorithms are implemented into the added bit file (keys) to reduce the file size. The RLE encoding algorithm is the first one, and the results were entered into the second compression round using the Huffman algorithm. The file is then attached to the header of the compressed image file. This proposed method reduces the entropy of the generated binary sequence so that it grants higher compression ratio. The experimental results show that the compression algorithm is efficient and gives a good compression ratio without losing data. The using of BCH code improves the results of Huffman in terms of increasing compression ratio.

Many of the standard compression methods are based, at their lowest level, in coding digital words of variable length or VLC, Huffman type, designed in 1952 or in any of its versions, as canonical. This article presents an VLC with greater efficiency than Huffman encoding, named as “Leveling”, which uses two variants, “Leveled Reordering” for low redundancy, e.g. for text and, “Segmented Leveling” for middle and high redundancy, for image processing. Leveling, developed by Javier Joglar in 1995, uses the concepts of “meaning” and “ordering” of the VLC codes generated, to get optimum performance in terms of “compression ratio”, higher than any other non-adaptive VLC.

In this paper, a simplified structure of integer lifting wavelet filter banks for lossless image compression is proposed by shifting and merging the scaling factors of the row and the column wavelet transforms. It is implemented by reducing the numbers of scaling factors and considering the scaling lifting. The numbers of scaling factors of the 2-D wavelet transform can be reduced by shifting and merging operation, and then the computing speed can be improved. Furthermore, the scaling lifting of simplified structure can be used to reduce the computing errors and get more accurate results. Experiments show that the simplified integer lifting structure results in lesser computational steps than the standard integer lifting structure and therefore improves the speed of the image compression. Besides, using the new lossless image compression system based on simplified integer lifting wavelet, the lower bit-rates are obtained.

New innovative research trends are more essential in the area of image compression for various imaging applications. These applications require good visual quality in processing. In general the tradeoff between compression efficiency and picture quality is the most important parameter to validate the work. The existing algorithms for still image compression were developed by considering the compression efficiency parameter by giving least importance to the visual quality in processing. Hence, we proposed a novel lossless image compression algorithm which efficiently suited for various types of digital images. Thus, in this work, we specifically address the following problem that is to maintain the compression ratio for better visual quality in the reconstruction and considerable gain in the values of Peak Signal to Noise Ratio (PSNR). We considered medical images , satellite extracted images and natural images for the inspection and proposed a novel procedure named as Novel Optimized Golomb Rice Coding (NOGR) to increase the visual quality of the reconstructed image. The result of the proposed technique outperforms present techniques and the results are simulated using MATLAB.

New innovative research trends are more essential in the area of image compression for various imaging applications. These applications require good visual quality in processing. In general the tradeoff between compression efficiency and picture quality is the most important parameter to validate the work. The existing algorithms for still image compression were developed by considering the compression efficiency parameter by giving least importance to the visual quality in processing. Hence, we proposed a novel lossless image compression algorithm which efficiently suited for various types of digital images. Thus, in this work, we specifically address the following problem that is to maintain the compression ratio for better visual quality in the reconstruction and considerable gain in the values of Peak Signal to Noise Ratio (PSNR) in two directions of research. We considered medical images , satellite extracted images and natural images for the inspection and proposed a novel procedure named as Novel Optimized Golomb Rice Coding (NOGR) to increase the visual quality of the reconstructed image. The result of the proposed technique outperforms present techniques and the results are simulated using MATLAB.

본 논문에서는 무손실 압축을 위하여 예측기를 스위칭하는 알고리즘을 제안한다. 제안하는 방법은 MED(median edge detector), GAP(gradient adaptive prediction) 예측기의 예측 에러들에 따라 적응적으로 하나의 예측기를 이용하여 화소값을 예측한다. 그리고, 에러는 기존의 엔트로피 방법을 이용하여 측정한다. 실험 결과, 제안하는 알고리즘이 기존 예측 방법보다 적은 에러값과 엔트로피를 가짐으로써 향상된 압축을 할 수 있음을 보인다.

In this paper, a predictor switching algorithm for lossless compression is proposed. It uses adaptively one of two predictors using errors obtained by MED(median edge detector) and GAP(gradient adaptive prediction). The reduced error is measured by existing entropy method. Experimental results show that the proposed algorithm can compress higher than existing predictive methods.

In this paper, we propose a new algorithm that improves the lossless data compression rate. The proposed algorithm lessens the redundancy and improves the compression rate evolutionarily around 40 up to 80 percentile depending on the characteristics of binary images used for compression. In order to demonstrate the superiority of the proposed method, the comparison between the proposed method and the LZ78 (LZ77) is demonstrated through experimental results theoretical analysis.

In this paper, we propose an efficient lossless compression algorithm using spatial and temporal information. The proposed method obtains higher lossless compression of images than other lossless compression techniques. It is divided into two parts, a motion adaptation based predictor part and a residual error coding part. The proposed nonlinear predictor can reduce prediction error by learning from its past prediction errors. The predictor decides the proper selection of the spatial and temporal prediction values according to each past prediction error. The reduced error is coded by existing context coding method. Experimental results show that the proposed algorithm has better performance than those of existing context modeling methods.