An Evaluation of Image Compression Algorithms for Colour Retinal Images

Future high resolution instruments planned by CNES for space remote sensing missions will lead to higher bit rates because of the increase in resolution and dynamic range. For example, the ground resolution improvement induces a data rate multiplied by 8 from SPOT4 to SPOT5 and by 28 to PLEIADES-HR. Lossy data compression with low complexity algorithms is then needed while compression ratio must considerably rise. New image compression algorithms have been used to increase their compression performance while complying with image quality requirements from the community of users and experts. Thus, DPCM algorithm used on-board SPOT4 was replaced by a DCT-based compressor on-board SPOT5. Recent compression algorithms such as PLEIADES-HR one use wavelet-transforms and bit-plane encoders. But future compressors will have to be more powerful to reach higher compression ratios. New transforms have been studied by CNES to exceed the DWT but other techniques as selective compression are required in order to obtain a significant performance gap. This chapter gives an overview of CNES past, present and future studies of on-board compression algorithms for high-resolution images.

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].

A multidimensional incremental parsing algorithm (MDIP) for multidimensional discrete sources, as a generalization of the Lempel-Ziv coding algorithm, is investigated. It consists of three essential component schemes, maximum decimation matching, hierarchical structure of multidimensional source coding, and dictionary augmentation. As a counterpart of the longest match search in the Lempel-Ziv algorithm, two classes of maximum decimation matching are studied. Also, an underlying behavior of the dictionary augmentation scheme for estimating the source statistics is examined. For an m-dimensional source, m augmentative patches are appended into the dictionary at each coding epoch, thus requiring the transmission of a substantial amount of information to the decoder. The property of the hierarchical structure of the source coding algorithm resolves this issue by successively incorporating lower dimensional coding procedures in the scheme. In regard to universal lossy source coders, we propose two distortion functions, the local average distortion and the local minimax distortion with a set of threshold levels for each source symbol. For performance evaluation, we implemented three image compression algorithms based upon the MDIP; one is lossless and the others are lossy. The lossless image compression algorithm does not perform better than the Lempel-Ziv-Welch coding, but experimentally shows efficiency in capturing the source structure. The two lossy image compression algorithms are implemented using the two distortion functions, respectively. The algorithm based on the local average distortion is efficient at minimizing the signal distortion, but the images by the one with the local minimax distortion have a good perceptual fidelity among other compression algorithms. Our insights inspire future research on feature extraction of multidimensional discrete sources.

Remote Healthcare Monitoring Systems (RHMs) that use ECG signals are very effective tools for the early diagnosis of various heart conditions. However, these systems are still confronted with a problem that reduces their efficiency, such as energy consumption in wearable devices because they are battery-powered and have limited storage. This paper presents a novel algorithm for the compression of ECG signals to reduce energy consumption in RHMs. The proposed algorithm uses discrete Krawtchouk moments as a feature extractor to obtain features from the ECG signal. Then the accelerated Ant Lion Optimizer (AALO) selects the optimum features that achieve the best-reconstructed signal. Our proposed algorithm is extensively validated using two benchmark datasets: MIT-BIH arrhythmia and ECG-ID. The proposed algorithm provides the average values of compression ratio (CR), percent root mean square difference (PRD), signal to noise ratio (SNR), Peak Signal to noise ratio (PSNR), and quality score (QS) are 15.56, 0.69, 44.52, 49.04 and 23.92, respectively. The comparison demonstrates the advantages of the proposed compression algorithm on recent algorithms concerning the mentioned performance metrics. It also tested and compared against other existing algorithms concerning Processing Time, compression speed and computational efficiency. The obtained results show that the proposed algorithm extremely outperforms in terms of (Processing Time = 6.89 s), (compression speed = 4640.19 bps) and (computational efficiency = 2.95). The results also indicate that the proposed compression algorithm reduces energy consumption in a wearable device by decreasing the wake-up time by 3600 ms.

In the rapidly developing information era, efficiency in data storage and transfer becomes crucial. This research aims to analyze the performance of various data compression algorithms in the context of data storage and transfer. A literature review was conducted to understand the theoretical basis, types of compression algorithms, and factors influencing performance. The research methodology involves controlled experiments to measure compression ratios, compression and decompression speeds, as well as algorithm adaptability to data variations. The research results provide deep insight into the efficiency of each algorithm in various situations. The conclusion highlights the advantages and disadvantages of specific algorithms, providing practical recommendations for algorithm selection based on application needs.

PurposeCone‐beam computerized tomography (CBCT) is routinely performed for verification of patient position in radiotherapy. It produced a large amount of data which require a method to compress them for efficient storage. In this study three video compression algorithms were introduced and their performance was evaluated based on real patient data.Materials and methodsAt first CBCT images in multiple sets of a patient were transferred from reconstruction workstation or exported from treatment planning system. Then CBCT images were sorted according to imaging time (time‐prioritized sequence) or imaging location (location‐prioritized sequence). Next, this sequence was processed by a video compression algorithm and resulted in a movie. Three representative video compression algorithms (Motion JPEG 2000, Motion JPEG AVI, and MPEG‐4) were employed and their compression performance was evaluated based on the CBCT data of 30 patients.ResultsAmong three video compression algorithms, Motion JPEG 2000 has the least compression ratio since it is a lossless compression algorithm. Motion JPEG AVI and MPEG‐4 have higher compression ratios than Motion JPEG 2000 but come with certain image losses. For MPEG‐4, location‐prioritized sequences show higher compression ratio than time‐prioritized sequences. Based on the results achieved on the clinical target verification application, the registration accuracy of CBCT after decompression was comparable to that of the original CBCT.ConclusionsVideo compression algorithms could provide a higher compression ratio comparing to static image compression algorithm. Although the loss of CBCT image due to compression its impact on registration accuracy of patient positioning is almost negligible. Video compression method is an effective way to substantially reduce the size of CBCT images for storage.

This paper addresses two questions concerning JPEG2000 compression – firstly – how much has noise influence on compression performance – secondly – can compression performance be improved by applying a new complementary conception with introducing a denoising process before the application of compression Indeed, radiographic images are a combination between the relevant signal and noise, which is per definition not compressible. The noise behaves generally close to a mixture of Gaussian and/or Poisson statistics, which generally affects the compression performance. In this paper, the influence of noise on the compression performance of JPEG2000 images with investigating the parameters signal dynamic and spatial pattern frequency are considered; and the JPEG2000 compression scheme combined with a denoising process is analyzed on simulated and real dental ortho-pan-tomographic images. The test images are generated using Poisson statistics; the denoising utilizes a Monte Carlo noise modeling method. A hundred selected images are denoised and the compression ratio, using lossless and lossy JPEG 2000, is reported and evaluated.

In this article we report on a study of how to use the context tree weighting (CTW) algorithm for lossless image compression. This algorithm has been shown to perform optimally, in terms of redundancy, for a wide class of data sources. Our study shows that this algorithm can successfully be applied to image compression even in its basic form. We also report on possible modifications of the basic CTW algorithm to let it work more efficiently for image data. Our research is briefly focussed on the compression of medical gray scale images.

Problem statement: Identifying the new lossless image compression algorithm for high performance applications like medical and satellite imaging; a high quality lossless image is most important when reproduction which leads to classify the data for decision making. Approach: A new lossless hybrid algorithm based on simple selective scan order with Bit Plane Slicing method is presented for lossless Image compression of limited bits/pixel images, such as medical images, satellite images and other still images common in the world. Efficient coding is achieved by run length and modified Huffman coding. This approach is combined with efficient selective scan order for entire image in one pass through. Results: The new hybrid algorithm achieves good compression rate, compared to the existing schemes of coding with different test images. Conclusion: Compared to the existing standards JPEG-LS and CALIC, the compression rate is reduced with our proposed algorithm for different standard test images.

Although subband and wavelet coding have been proved to be very efficient for lossy image compression, it is generally not possible to represent the output of most subband/wavelet filters by a limited number of bits and yet guarantee perfect reconstruction. This is a fundamental problem when applying subband and wavelet coding to lossless image compression. This paper presents a solution to this problem by an investigation of interpolative subband coding. Based on an evaluation of the decorrelation performance of some typical image interpolation techniques, a new interpolative coding algorithm has been proposed. The new algorithm has been shown to be superior to two related algorithms, the hierarchical interpolation algorithm and the recently published minimum entropy pyramid algorithm.

In spite of rapid improvements in network bandwidth and costs, conventional remote desktop services are still not suitable for high-end graphical applications such as CAD software. Complexity of graphical images with multiple vectors and frequent updates of the large parts of the display make it difficult to provide remote desktop service with the visually permissible quality for engineering tasks. In this paper, we present the "Remote Virtual Environment Computing" (RVEC). RVEC combines movie compression and still-image loss less compression algorithm to reduce drastically the bandwidth of the image transmission without deteriorating visual quality below an acceptable engineering standard. Furthermore, RVEC uses a novel loss less compression algorithm, referred to as the graphics compression to compress the still image of a graphical application. The graphics compression makes use of vector features in the display image, thereby achieving the better compression ratio without a negative effect on the compression speed. We give detailed comparison of RVEC against a variety of existing technologies, including VNC, RDP, and RGS. We demonstrate that RVEC can provide up to five times faster response than existing technologies while reducing the bandwidth to roughly one-tenth. RVEC enables a practical remote desktop service of high-end graphical applications, leading to a next generation manufacturing environment, referred to as the "ENGINEERING CLOUD". The ENIGNEERINGCLOUD can provide ubiquitous access to a wide range of engineering applications from a cloud data center.

Data compression is an important part of information security because compressed data is more secure and easy to handle. Effective data compression technology creates efficient, secure, and easy-to-connect data. There are two types of compression algorithm techniques, lossy and lossless. These technologies can be used in any data format such as text, audio, video, or image file. The main objective of this study was to reduce the physical space on the various storage media and reduce the time of sending data over the Internet with a complete guarantee of encrypting this data and hiding it from intruders. Two techniques are implemented, with data loss (Lossy) and without data loss (Lossless). In the proposed paper a hybrid data compression algorithm increases the input data to be encrypted by RSA (Rivest-Shamir-Adleman) cryptography method to enhance the security level and it can be used in executing lossy and lossless compacting Steganography methods. This technique can be used to decrease the amount of every transmitted data aiding fast transmission while using slow internet or take a small space on different storage media. The plain text is compressed by the Huffman coding algorithm, and also the cover image is compressed by Discrete wavelet transform DWT based that compacts the cover image through lossy compression in order to reduce the cover image's dimensions. The least significant bit LSB will then be used to implant the encrypted data in the compacted cover image. We evaluated that system on criteria such as percentage Savings percentage, Compression Time, Compression Ratio, Bits per pixel, Mean Squared Error, Peak Signal to Noise Ratio, Structural Similarity Index, and Compression Speed. This system shows a high-level performance and system methodology compared to other systems that use the same methodology.

This study presents insights into the computational complexity of fractal image compression (FIC) algorithms. Unlike JPEG, a fractal encoder necessitates more CPU time in contrast to the decoder. The study examines various factors that impact the encoder and its computational cost. Many researchers have dedicated themselves to the field of fractal encoding to overcome the computational cost of the FIC algorithm. Here, this study offers a look over the approaches in the aspect of time complexity. The automated baseline fractal compression algorithm is studied to demonstrate the understanding of delay in the encoder. The study establishes how various approaches trade‐off between the quality of decoder, compression ratio, and CPU time. The experiment section shows the bargain between fidelity criteria of the baseline algorithm.

Set Partitioning in Hierarchical Trees (SPIHT) is an effective image compression algorithm. SPIHT is a unique approach which transmits the values of the pixels indirectly to the coordinates. The packet coders in the images present a universal transform the framework in a constriction of the filter bank schema which is attained by the Space Frequency Quantization (SFQ) and the effective compression is achieved by the Huffman coding. The proposed scheme permits transformation in the joints and alterations in the discrete magnitude of the input image. Hence, coding performance is enhanced and the image is compressed without compromising the excellence of the image. MSPIHT is an enhanced algorithm that fits the requirement of medical image compression for storage and transmission of medical data. MSPIHT improves on the standard SPIHT in the encoding operation of wavelet coefficients to provide high quality images at low bit rates important in medical applications where accuracy is paramount. The modification is aimed at changing the set partitioning strategy by minimizing memory consumption rates, thus improving the compression ratio and speed. It also may be used at telemedicine where high image compression rate is required for the quick and efficient image transfer for diagnostics and consultations. Experimental results shows that the image compression scheme is improved with the SPIHT-SFQ based Huffman Coding scheme and it outperforms the current image compression algorithms.

In technological developments, it also affects the size of the data from a file, especially in transmitting JPEG images or images. Digital images have a size or capacity that is large enough so that the image transmission process often experiences problems due to its large enough capacity. Large data sizes also require large storage space, therefore additional solutions are needed to save space on storage media, such as reducing the size of the data to be stored. Knowing the value of the speed and efficiency of the JPEG image compression process needs to remember so many compression methods that exist today, one of which is the FELICS method. Image compression is carried out on images aimed at reducing the redundancy of existing data in the image so that it can be stored or transmitted efficiently. The FELICS method, which stands for Fast Compression & Lossless Image System, is a lossless image compression algorithm that performs 5 times faster than the original lossless JPEG codec and produces a compression ratio of the same value. So through this research the writer wants to carry out the analysis process of the FELICS method. The Fast Compression & Lossless Image System method works very well on images and produces high ratio values. Keywords : Analysis, Image, Compression, FELICS Method