주파수 영역에서 움직임 예측을 이용한 디지털 홀로그램 압축 기법

We propose a CGH (computer-generated hologram) lossless compression method that divides hologram data into blocks using a quadtree. The compression ratio we achieved was higher than that of previous studies.

This paper proposes a method for improving the image quality when we use low-bit motion estimation (ME) using linear quantization. By using edge enhanced images for quantization, we can increase the accuracy of the low-bit ME and improve the image quality. It is known that using low-bit images for ME is effective on saving power but it slightly degrades image quality. The quality of the encoded image depends on the thresholds for data quantization, and algorithms for determining thresholds are studied. The proposed method uses linear quantization, which simply truncates the least significant bits. This method is simple without any complicated threshold calculations, and the image quality improves as much as the methods with threshold calculations. To evaluate the effectiveness, we simulate results for image quality and estimate the power consumption using synthesis results from a VHDL model motion estimator.

In this paper, Motion Estimation (ME) and Motion Compensation (MC) techniques are used for video compression which eliminates the temporal redundancy of video sequence. For motion estimation, Block Matching (BM) algorithm has been used among the various motion estimation algorithms because of its simplicity and efficiency. In this paper, the performance of block matching algorithm such as Four Step Search (4SS) has been improved using Block Transform Scaling (BTS). In BTS all the varying nature frames of video transform into uniform frames using adaptive filter, which fills the pixel information where pixel value is missing within the frame. The proposed work mainly focuses on the compression of videos which have medical information in it. At the end of this paper the result analysis has been done using the parameters such as Peak Signal-to-Noise Ratio (PSNR), Mean Square Error (MSE), Computation Time (CT) and Compression Ratio (CR).

This paper explores a compression method for the phase component of computer-generated hologram (CGH) data. The proposed method utilizes the framework of JPEG-LS which is an international standard for lossless image compression. The phase component can be regarded as a signal distributed on the unit circle. However, its image representation introduces discontinuities around values of 0 and 2π, even though these values are adjacent on the circle. Such discontinuities significantly degrade coding efficiency when conventional image compression techniques are applied. To address this issue, we modify the MED predictor used in the JPEG-LS standard to accommodate the discontinuous nature of the phase component.

Different medical devices for imaging used by centers and clinics produce an increasing number of sequential medical images. ‎Different imaging techniques such as Computed Tomography (CT), Magnetic Resonance Imaging (MRI) and Fluoroscopy ‎produce a set of series for the same patient. Within these images, most of the image parts are fixed against noticeable changes ‎in the remaining part. This consumes non-ignorable storage space. This paper proposes a near-lossless compression ‎technique that considers the fixed image parts to focus on changing parts for a higher compression ratio. In some applications, lossless compression techniques are highly preferable against preferring lossy techniques in some applications. In other applications, near-lossless compression techniques are preferable to lossless and lossy compression techniques, where lossy ones may ‎lose significant details, and the lossless ones produce less compression ratios than near-lossless ones. Previous works dealt with Fluoroscopy images as individual images or using ‎video compression techniques. This work tends to handle the whole series of ‎images as an integrated object. This paper considers subtracting successive ‎images to detect ROI areas producing zero overall values over similar ‎areas and non-zero ones within ROI ones. The double coding technique and near-lossless concept of compression increase the compression ratio. ‎Conducted experiments showed encouraging results benchmarking the other published ‎works in medical image compression.‎

This paper describes the compression techniques to attain high bandwidth and memory requirements which is necessary in high speed embedded systems. The most commonly used bitstream compression technique is Dictionary based code compression (DCC) due to its high compression. In this technique, the bitstream which is not stored in the dictionaries generates it as an uncompressed format that needs large storage space. To mitigate this issue, two effective compression techniques named as Separated split LUT (SSL) and Bitmasked Separated split LUT (BSSL) are proposed to improve the compression ratio (CR) and bit saving rate (BSR).The proposed compression algorithms are coded in VHDL, synthesized using Xilinx ISE 14.2 and implemented in Kintex 7 Field Programmable Gate Array (FPGA).The proposed compression techniques are evaluated for various benchmark processors such as Sha, CRC, Dijkstra, FFT and Susan processor. Sha processor has the lowest bitstream entries and the CR is 59.75% and the CR for CRC, Dijkstra, FFT and Susan processor are 58, 45, 70 and 75, respectively.

The H.264 video compression standard uses enhanced Motion Estimation (ME) features to improve both the compression ratio and the quality of compressed video. The two primary enhancements are the use of Variable Block Size Motion Estimation (VBSME) and multiple reference frames. These two additions greatly increase the computational complexity of the ME algorithm, to the point where a software based real-time (30 frames per second (fps)) implementation is not possible on present microprocessors. Thus hardware acceleration of the H.264 ME algorithm is necessary in order to achieve real-time performance for the implementation of the VBSME and multiple reference frames features. This thesis presents a scalable FPGA-based ME architecture that supports real-time H.264 ME for a wide range of video resolutions ─ from 640x480 VGA to 1920x1088 High Definition (HD). The architecture contains innovations in both the data-path design and memory organization to achieve scalability and real-time performance on FPGAs. At 37% FPGA device utilization, the architecture is able to achieve 31 fps performance for encoding full 1920x1088 progressive HDTV video.

Space research organizations, hospitals and military air surveillance activities, among others, produce a huge amount of data in the form of images hence a large storage space is required to record this information. In hospitals, data produced during medical examination is in the form of a sequence of images and are very much correlated; because these images have great importance, some kind of lossless image compression technique is needed. Moreover, these images are often required to be transmitted over the network. Since the availability of storage and bandwidth is limited, a compression technique is required to reduce the number of bits to store these images and take less time to transmit them over the network. For this purpose, there are many state-of the-art lossless image compression algorithms like CALIC, LOCO-I, JPEG-LS, JPEG20000; Nevertheless, these compression algorithms take only a single file to compress and cannot exploit the correlation among the sequence frames of MRI or CE images. To exploit the correlation, a new algorithm is proposed in this paper. The primary goals of the proposed compression method are to minimize the memory resource during storage of compressed data as well as minimize the bandwidth requirement during transmission of compressed data. For achieving these goals, the proposed compression method combines the single image compression technique called super spatial structure prediction with inter-frame coding to acquire grater compression ratio. An efficient compression method requires elimination of redundancy of data during compression; therefore, for elimination of redundancy of data, initially, the super spatial structure prediction algorithm is applied with the fast block matching approach and later Huffman coding is applied for reducing the number of bits required for transmitting and storing single pixel value. Also, to speed up the block-matching process during motion estimation, the proposed method compares those blocks that have identical sum and leave the others; therefore, the time taken by the block-matching process is reduced by minimizing the unnecessary overhead during the blockmatching process. Thus, in the proposed fast lossless compression method for medical image sequences, the twostage redundant data elimination process ultimately reduces the memory resource required for storing and transmission. The method is tested on the sequences of MRI and CE images and produces an improved compression rate.

With the resolution enhancement techniques such as OPC (Optical Proximity Correction) and SRAF (Sub-Resolution Assist Features), the size of layout data have grown significantly. It is quite common now to find layout files that are tens of GBs in size. Unlike GDSII which can store data hierarchically, mask data formats such as MEBES are essentially flat and more voluminous. Moreover, polygonal data present in layout data files is fractured, thereby increasing the data volume before getting stored in MEBES data format. This results in huge MEBES files. As per the ITRS roadmap of 2005, for a 45nm half-pitch node that is expected to be in use by 2010, the mask data volume for a single layer is expected to reach up to 825 GB. Storing and transferring such large mask data are issues for which the mask industry needs solutions. Historically, MEBES is the most prevalent EB format in the industry. Moreover, in many Mask Data Preparation (MDP) flows, the MEBES format is being used as the de-facto standard for specifying the fractured EB data even though the final target EB machine might be different. In this paper we present techniques for lossless reversible compression of MEBES data, i.e., when the compressed file is decompressed, the generated uncompressed file matches the original MEBES file bit- by-bit. By applying these compression techniques a compression ratio of 5X to 15X can be obtained. In practice, compressing MEBES files is usually a one-time task, but decompression of compressed files is expected to be done multiple times as every time a compressed MEBES file needs processing, it has to be decompressed. MEBES is essentially an efficient data format and the geometries are stored compactly. As a result the compression/decompression techniques described in this paper are quite computation intensive in order to achieve higher compression ratio. This in turn leads to higher CPU time for compression/decompression compared to generic compressors such as gzip. However, as the format-specific compressors produce higher compression ratios, the disk I/O time for compression and decompression is expected to be less compared to the generic compressors such as gzip and gunzip. In spite of this, the format-specific decompressor is usually 3-5X times slower than the generic decompressor such as gunzip. Since decompression is expected to be done more frequently as compared to compression, speeding up decompression is highly desirable. We present a compression technique for MEBES data which enables multiple process threads to decompress the compressed MEBES data. With the multi-core multiprocessor machines becoming quite inexpensive and common, the multi-threaded decompression is expected to perform close to the disk I/O time on such machines. The paper details out the techniques, experimental results in terms of comparative compression ratios, compression and decompression speed using single threads and multiple threads. The possibility of getting higher compression ratios as well as higher or comparable decompression speed makes it more practical to use format specific reversible compression schemes rather than using generic compressors. Even though, the paper focuses on compression and decompression of MEBES, it can be easily extended to the compression of GDSII [1].

In this paper, the technique of quasi-lossless compression based on the image restoration is presented. The technique of compression described in the paper includes three steps, namely bit compression, correlation removing and image restoration based on the theory of modulation transfer function (MTF). The quasi-lossless compression comes to a high speed. The quality of the reconstruction image under restoration is up to par of the quasi-lossless with higher compression ratio. The experiments of the TM and SPOT images show that the technique is reasonable and applicable.

This paper presents a promising time-domain based lossless compression technique called Log2 Sub-band encoding, which is designed for using on wireless biomedical devices. Data compression can help to save power from the wireless transceiver during data transmission, and from the storage medium during reading and writing, ultimately leading to a longer battery life of the device. The performance of Log2 Sub-band is measured in terms of its compression ratio (CR) on EEG data and its power consumption. Our simulation results indicate a CR that is comparable and even superior to the well-known Huffman coding, whilst consuming minimal hardware resource. The simulations primarily use electroencephalogram (EEG) data, and the power consumption during compressing process is given to evaluate the system's improvement on its power performance. The possible influence of different biomedical data on technique's performance will also be considered and the signal classification potential of Log2 Sub-band will be noted.

The future of healthcare delivery systems and tele medical applications will undergo a radical change due to the developments in wearable technologies, medical sensors, mobile computing and communication techniques. E-health was born with the integration of networks and telecommunications when dealing with applications of collecting, sorting and transferring medical data from distant locations for performing remote medical collaborations and diagnosis. Energy requirement is central to many battery-constrained systems, but they are essential in body sensor networks (BSNs) due to energy and resource constraints, and the critical nature of many healthcare applications. On-node signal processing and compression techniques can save energy by greatly reducing the amount of data transmitted over the wireless channel, but lossy techniques can incur a reduction in application. In this paper we are proposing an aggregation of Deflate and RLE algorithm called Aggregated Deflate RLE (ADR). ADR compression algorithm which provides better compression ratio with minimum compression time as compared to other existing compression techniques like LZW, Huffman and Deflate. We provide here various comparative result of our algorithm with above compression techniques which proves that we are providing best technique for compression of body sensor network data.

Modern scientific technology such as particle accelerators, telescopes, and supercomputers are producing extremely large amounts of data. That scientific data needs to be processed by using systems with high computational capabilities such as supercomputers. Given that the scientific data is increasing in size at an exponential rate, storing and accessing the data are becoming expensive in both time and space. Most of this scientific data is stored by using floating point representation. Scientific applications executed on supercomputers spend a large amount of CPU cycles reading and writing floating point values, making data compression techniques an interesting way to increase computing efficiency. Given the accuracy requirements of scientific computing, we only focus on lossless data compression. In this paper we propose a masking technique that partially decreases the entropy of scientific datasets, allowing for a better compression ratio and higher throughput. We evaluate several data partitioning techniques for selective compression and compare these schemes with several existing compression strategies. Our approach shows up to 15% improvement in compression ratio while reducing the time spent in compression by half time in some cases.

The numerous medical images generated by various imaging devices require a huge amount of storage space and minimum transmission bandwidth. So, it is necessary to use an appropriate technique for compression of medical images in order to meet the above concerns. This paper proposes a technique for lossless compression of significant parts by extracting the region of interest in DICOM images. The extracted region is compressed using Huffman encoding and genetic algorithm for the enhancement of compression ratio. Additionally, the value of PSNR has also been calculated. When the results of the proposed technique are compared with the previous method, it is found that there is an increase of more than 400% in the compression ratio. It also provides an average increase of 20% in the compression ratio by using Huffman encoding.

This technical summary gives a scientific technique to evaluate the effect of photograph healing on virtual image processing with the usage of a compression technique. First, a contrast among specific strategies of compression is carried out. It includes an analysis of Lossy and Lossless compression tactics for various photo report codecs, which include JPEG, PNG, GIF, and TIFF. This paper aims to evaluate the impact of image restoration on digital image processing by applying a compression technique. The study proposes a novel method that combines image compression and restoration processes to enhance image quality while reducing storage requirements. Various measures, including peak signal-to-noise ratio, structural similarity index, and compression ratio, are used to evaluate the effectiveness of the proposed method. The experimental results show that the joint approach of compression and restoration not only improves the image quality but also significantly reduces the file size. This indicates the potential of using image restoration as a preprocessing step in digital image processing to improve the overall performance of image compression algorithms. Additionally, the study discusses the computational efficiency of the proposed method and provides insights for further improvements. The results obtained offer a perception of the general impact of photo restoration on virtual photo processing, which might be beneficial for exceptional aspects, inclusive of excellent management, analysis, and recordspushed evaluation.