Acceptable Compression Ratios Research Articles

Single pixel imaging at high pixel resolutions.

The usually reported pixel resolution of single pixel imaging (SPI) varies between 32 × 32 and 256 × 256 pixels falling far below imaging standards with classical methods. Low resolution results from the trade-off between the acceptable compression ratio, the limited DMD modulation frequency, and reasonable reconstruction time, and has not improved significantly during the decade of intensive research on SPI. In this paper we show that image measurement at the full resolution of the DMD, which lasts only a fraction of a second, is possible for sparse images or in a situation when the field of view is limited but is a priori unknown. We propose the sampling and reconstruction strategies that enable us to reconstruct sparse images at the resolution of 1024 × 768 within the time of 0.3s. Non-sparse images are reconstructed with less details. The compression ratio is on the order of 0.4% which corresponds to an acquisition frequency of 7Hz. Sampling is differential, binary, and non-adaptive, and includes information on multiple partitioning of the image which later allows us to determine the actual field of view. Reconstruction is based on the differential Fourier domain regularized inversion (D-FDRI). The proposed SPI framework is an alternative to both adaptive SPI, which is challenging to implement in real time, and to classical compressive sensing image recovery methods, which are very slow at high resolutions.

Design and Control of a Vapour Recompression C3 Splitter

Vapor recompression (VRC) based heat integration is usually employed for purifying a propylene-propane mixture in a C3 splitter. A fixed-speed drive (FSD) or a variable-speed drive (VSD) compressor may be employed for overhead vapor recompression. In this work, economic controllable designs for the two compressor types in a VRC C3 splitter are obtained via rigorous steady state and pressure driven closed loop dynamic simulations. Results indicate that for FSD compression, significant pressure drop must be provided across the compressor suction throttling valve for sufficient rangeability towards worst-case disturbance rejection. Accordingly, the column pressure and necessary compression ratio are higher for an overall more expensive process compared to a VSD compressor. The VSD compressor design corresponds to the lowest condenser temperature with water as coolant and the reboiler temperature driving force at the minimum constraint. In the FSD compressor design, the condenser temperature must be increased sufficiently above the minimum for an acceptable compression ratio. The case study highlights the importance for considering control at the conceptual design stage for designing a robust operable process.

Optimization of image compression and ciphering based on EZW techniques

This paper presents the design and optimization of image compression and ciphering depend on optimized embedded zero tree of wavelet (EZW) techniques. Nowadays, the compression and ciphering of image have become particularly important in a protected image storage and communication. The challenge is put in application for both compression and encryption where the parameters of images such as quality and size are critical in secure image transmission. A new technique for secure image storage and transmission is proposed in this work. The compression is achieved by remodel the EZW scheme combine with discrete cosine transform (DCT). Encrypted the XOR ten bits by initial threshold of EZW with random bits produced from linear-feedback shift register (LFSR). The obtained result shows that the suggested techniques provide acceptable compression ratio, reduced the computational time for both compression and encryption, immunity against the statistical and the frequency attacks.

Efficient Low-Resource Compression of HIFU Data

Large-scale numerical simulations of high-intensity focused ultrasound (HIFU), important for model-based treatment planning, generate large amounts of data. Typically, it is necessary to save hundreds of gigabytes during simulation. We propose a novel algorithm for time-varying simulation data compression specialised for HIFU. Our approach is particularly focused on on-the-fly parallel data compression during simulations. The algorithm is able to compress 3D pressure time series of linear and non-linear simulations with very acceptable compression ratios and errors (over 80% of the space can be saved with an acceptable error). The proposed compression enables significant reduction of resources, such as storage space, network bandwidth, CPU time, and so forth, enabling better treatment planning using fast volume data visualisations. The paper describes the proposed method, its experimental evaluation, and comparisons to the state of the arts.

Lossy Data Compression Effects on Wall-bounded Turbulence: Bounds on Data Reduction

Postprocessing and storage of large data sets represent one of the main computational bottlenecks in computational fluid dynamics. We assume that the accuracy necessary for computation is higher than needed for postprocessing. Therefore, in the current work we assess thresholds for data reduction as required by the most common data analysis tools used in the study of fluid flow phenomena, specifically wall-bounded turbulence. These thresholds are imposed a priori by the user in L2-norm, and we assess a set of parameters to identify the minimum accuracy requirements. The method considered in the present work is the discrete Legendre transform (DLT), which we evaluate in the computation of turbulence statistics, spectral analysis and resilience for cases highly-sensitive to the initial conditions. Maximum acceptable compression ratios of the original data have been found to be around 97%, depending on the application purpose. The new method outperforms downsampling, as well as the previously explored data truncation method based on discrete Chebyshev transform (DCT).

COMPARTIVE ANALYSIS ON DICOM IMAGE COMPRESSION BASED ON WAVELET BASED TECHNIQUES

Medical image compression serves an important role in hospitals and medical centers as they concentrate on acquiring digital images for various medical applications .Image compression results in reducing the size of the files for effective storage requirement during the diagnosis of relevant clinically critical information. Image compression can be classified into two types namely lossy compression and lossless compression .Lossy compression techniques are avoided for medical image compression as it is concerned with critical regions which are termed as region of interest because they result in the loss of useful medical information and also process such as enhancement can reduce the information content of an image in lossy compression. Lossy compression techniques compress images with the high compression ratio. Lossless coding techniques compress images with acceptable compression ratio. . Digitized medical images are to be compressed by method which overcomes the redundancy of the images and decreases the number of the bits required to represent the medical image with acceptable information loss of the decoded image. Restorative Imaging confronts the difficulties of having pressure calculation which can diminish the loss of data in a picture, in the meantime it expands the pressure rate for lessening of capacity prerequisite and the span for transmission and this gets to be productive .This paper highlights the correlation of pressure methods, for example, jpeg2000,jpeg-ls and Lsphit calculation on the premise of pressure proportion and the nature of the picture after pressure.

Acceptable compression ratio of full-field digital mammography using JPEG 2000

To estimate the acceptable compression ratio of full-field digital mammography (FFDM) using the Joint Photographic Experts Group (JPEG) 2000 compression algorithm. Eighty cases that included images of 40 masses (20 benign, 20 malignant) and 40 microcalcifications (20 benign, 20 malignant) were collected. The images were compressed to five different lossy ratios: 20:1, 40:1, 60:1, 80:1, and 100:1, and four radiologists independently determined whether the compressed group was distinguishable from the control group. The ratio of the compressed group that was rated indistinguishable from the control group was compared for each reviewer, and the results were analysed for agreements of three or more reviewers. The ability to distinguish the compressed image from the control group is given as a range across the four reviewers: 0-1.3% (0/80 to 1/80) of the 20:1, 0-2.5% (0/80 to 2/80) of the 40:1, 5-7.5% (4/80 to 6/80) of the 60:1, 10-37.5% (8/80 to 30/80) of the 80:1, and 30-87.5% (24/80 to 70/80) of the 100:1. For three compression groups (20:1, 40:1, and 60:1), three or more reviewers agreed that there was a distinguishable difference for 0/80, 0/80, and 3/80 images, respectively. Thus, the compressed images do not differ significantly from the control group (p>0.05). However, the 80:1 and 100:1 compressed images were different for 9/80 and 29/80 images, respectively, which is significantly different from the control group (p<0.05). The lossy 60:1 compression ratio for FFDM is visually identical to the control image and, therefore, potentially acceptable for primary interpretation.

Quick outlier-resilient entropy coder for space missions

More than a decade has passed since the Consultative Committee for Space Data Systems (CCSDS) made its recommendation for lossless data compression. The CCSDS standard is commonly used for scientific missions because it is a general-purpose lossless compression technique with a low computational cost which results in acceptable compression ratios. At the core of this compression algorithm it is the Rice coding method. Its performance rapidly degrades in the presence of outliers, as the Rice coder is conceived for noiseless data following geometric distributions. To overcome this problem we present here a new entropy coder, the so-called Prediction Error Coder (PEC), as well as its fully adaptive version (FAPEC) which we show is a reliable alternative to the CCSDS standard. We show that PEC and FAPEC achieve high compression ratios even when a large amount of outliers are present in the data. This is done by testing our compressors with synthetic and real data, comparing the compression ratios and processor requirements with those obtained using the CCSDS standard.

What Is Wrong with Compression Ratio in Lossy Image Compression?

Editor: We read with interest the article by Dr Ringl and colleagues (1) in the September 2006 issue of Radiology, in which they evaluate the effect of using the lossy image-compression algorithm, Joint Photographic Experts Group (JPEG) 2000, on computed tomographic (CT) images. We found the most interesting part of the article to be in the Discussion section, in which the authors stated that application of the highest acceptable compression ratio (CR) might be restricted because the quality of lossy compressed images depends on the level of image noise and the structural content of the image. We would like to further highlight this important issue that has been often neglected in many studies evaluating lossy image compression in radiology. For diagnostic accuracy, it is of utmost importance that the amount of image detail preservation in lossy image compression is constant, predictable, and controlled. Image compression can be performed by means of two compression modes—namely, a constant quality factor (QF) and a constant CR. The effects of these modes on image detail degradation were studied recently (2). It was demonstrated that image detail degradation greatly depends on image content in a constant CR compression mode. In other words, more image details would be lost on the more complex images and vice versa. On the other hand, constant degree of image detail degradation, which is crucial for diagnostic accuracy, was achieved only with a QF compression mode. However, the scales for adjusting QF with the most often used JPEG and JPEG 2000 lossy compression methods are not standardized, although the methods themselves are ISO (International Organization for Standardization) standards, which makes comparative evaluation of different lossy compression methods infeasible (3). This fact could be the reason that CR has become a de facto standard for expressing the degree of image data reduction in radiology, and it is even used in the U.S. Food and Drug Administration guidance (4). Nevertheless, it has to be emphasized that CR depends not only on the degree of data reduction but also on the image content of original image (2) and noise (5), which is in accordance with the theory of data compression (6). In conclusion, CR is by definition a measure of file size reduction and as such is not suitable to expressing the degree of image detail degradation in lossy image compression.

Wavelet compression algorithm applied to abdominal ultrasound images

AbstractWe sought to investigate acceptable compression ratios of lossy wavelet compression on 640 × 480 × 8 abdominal ultrasound (US) images. We acquired 100 abdominal US images with normal and abnormal findings from the view station of a 932‐bed teaching hospital. The US images were then compressed at quality factors (QFs) of 3, 10, 30, and 50 followed outcomes of a pilot study. This was equal to the average compression ratios of 4.3:1, 8.5:1, 20:1 and 36.6:1, respectively. Four objective measurements were carried out to examine and compare the image degradation between original and compressed images. Receiver operating characteristic (ROC) analysis was also introduced for subjective assessment. Five experienced and qualified radiologists as reviewers blinded to corresponding pathological findings, analysed paired 400 randomly ordered images with two 17‐inch thin film transistor/liquid crystal display (TFT/LCD) monitors. At ROC analysis, the average area under curve (Az) for US abdominal image was 0.874 at the ratio of 36.6:1. The compressed image size was only 2.7% for US original at this ratio. The objective parameters showed the higher the mean squared error (MSE) or root mean squared error (RMSE) values, the poorer the image quality. The higher signal‐to‐noise ratio (SNR) or peak signal‐to‐noise ratio (PSNR) values indicated better image quality. The average RMSE, PSNR at 36.6:1 for US were 4.84 ± 0.14, 35.45 dB, respectively. This finding suggests that, on the basis of the patient sample, wavelet compression of abdominal US to a ratio of 36.6:1 did not adversely affect diagnostic performance or evaluation error for radiologists' interpretation so as to risk affecting diagnosis

Information hiding based on search-order coding for VQ indices

Compared with various information-hiding techniques on the spatial domain proposed previously, VQ-based information hiding technique has not been paid much attention. As a result, we present a steganographic scheme based on the search-order coding (SOC) compression method of vector quantization (VQ) indices in this paper. The major goal is to embed secret data into the compression codes of the host image such that the interceptors will not notice the existence of secret data. In the proposed scheme, the embedding process induces no extra coding distortion and adjusts the bit rate according to the size of secret data. According to the experimental results, it is confirmed that the proposed scheme yields a good and acceptable compression ratio of the image. In addition, the receiver can efficiently receive both the compressed image and the embedded data almost at the same time.

Information hiding based on search-order coding for VQ indices

Compared with various information-hiding techniques on the spatial domain proposed previously, VQ-based information hiding technique has not been paid much attention. As a result, we present a steganographic scheme based on the search-order coding (SOC) compression method of vector quantization (VQ) indices in this paper. The major goal is to embed secret data into the compression codes of the host image such that the interceptors will not notice the existence of secret data. In the proposed scheme, the embedding process induces no extra coding distortion and adjusts the bit rate according to the size of secret data. According to the experimental results, it is confirmed that the proposed scheme yields a good and acceptable compression ratio of the image. In addition, the receiver can efficiently receive both the compressed image and the embedded data almost at the same time.

Ultrasound and angio image compression by cosine and wavelet transforms

The investigation results for improving lossy compression techniques for ultrasound and angio images are presented. The goal was to determine where the compression process could be improved for the medical application, and to make efforts to improve it. It is proved that the wavelet transform outperforms the discrete cosine transform applied to ultrasound and angio images. A lot of wavelet classes were tried for choosing the best one suited for corresponding image classes, which were characterised by a content complexity criterion. The analysis of international image compression standards was carried out. Special attention was paid to an algorithmical and high level service structure of a new still image compression standard JPEG2000. Its open architecture enables including some wavelet classes which we would like to suggest for medical images. A set of recommendations for acceptable compression ratio for different medical image modalities was developed. It was carried out on the base of compression study performed by the group of angiologists and cardiologists .

Assessment of commercial compression algorithms, of the lossy DCT and lossless types, applied to diagnostic digital image files

The need for diagnostic image compression of the lossy or irreversible type has been declining due to the rapid increase in commercially available formatted hard disk capacity. It is estimated that the latter has increased about three orders of magnitude in the past 14 years while the size of diagnostic image files has, of course, remained constant. During the same period, despite claims for significantly improved performancy by vendors, it seems that only small progress has been made in commercial lossless and lossy compression algorthms. There is still no consensus for lossy compression to a level acceptable for diagnosis. This is mostly considered to be around a ratio of 10:1. However, acceptable compression ratios depend heavily on the type of images processed and may be compared with the 3:1 ratio produced by lossless algorithms. This last value was shown to increase to more than 5.5:1 for γ-camera images when corrected for the noise content of individual bit planes and for the display capabilities of computer monitors. Therefore, any possible benefits of lossy over lossless compression become questionable when the currently available hard disk capacity and network transmission speed are considered against the inevitable loss of information in the lossy type of compression.

Detection of subtle interstitial abnormalities of the lungs on digitized chest radiographs: acceptable data compression ratios.

To determine acceptable compression ratios for digital radiography, we evaluated the effect of data compression on the detection of subtle interstitial lung abnormalities using digitized chest radiographs. Screen-film chest radiographs of 38 patients with subtle interstitial lung abnormalities and 40 patients with normal lung parenchyma were digitized (spatial resolution, 0.175 mm; 2000 x 2000 pixels; 10 bits per pixel) and compressed with the discrete cosine transform method at ratios of 10:1, 20:1, and 30:1. Five chest radiologists and five radiology residents examined the uncompressed and compressed digital images and rates the presence of interstitial lung abnormalities with a five-level scale of confidence. Results were analyzed by receiver operating characteristic methods. Overall, the interpretation of images with a compression ratio of 30:1 was significantly less accurate than that of uncompressed images (p < .05). For the five chest radiologists, interpretation of images with a compression ratio of 20:1 or 30:1 was significantly less accurate than that of uncompressed images (p < .05). However, for the five residents, no significant difference between interpretations of compressed and uncompressed images was noted (p > or = .05). These results suggest that a 10:1 data compression ratio does not influence the detection of subtle interstitial lung abnormalities. However, information that is lost with a 20:1 data compression ratio might be essential for interpretation by experienced chest radiologists.

Radiological image compression using error-free irreversible two-dimensional direct-cosine-transform coding techniques.

Some error-free and irreversible two-dimensional direct-cosine-transform (2D-DCT) coding, image-compression techniques applied to radiological images are discussed in this paper. Run-length coding and Huffman coding are described, and examples are given for error-free image compression. In the case of irreversible 2D-DCT coding, the block-quantization technique and the full-frame bit-allocation (FFBA) technique are described. Error-free image compression can achieve a compression ratio from 2:1 to 3:1, whereas the irreversible 2D-DCT coding compression technique can, in general, achieve a much higher acceptable compression ratio. The currently available block-quantization hardware may lead to visible block artifacts at certain compression ratios, but FFBA may be employed with the same or higher compression ratios without generating such artifacts. An even higher compression ratio can be achieved if the image is compressed by using first FFBA and then Huffman coding. The disadvantages of FFBA are that it is sensitive to sharp edges and no hardware is available. This paper also describes the design of the FFBA technique.

Compression of radiological images with 512, 1,024, and 2,048 matrices.

A comprehensive study was performed using the full-frame bit-allocation compression technique on 78 radiological images, including digitized radiographs, computed radiographs, and computed tomography images. Each radiograph was digitized to 2,048 X 2,048 X 10, 1,024 X 1,024 X 10, and 512 X 512 X 10 matrices, respectively, with a laser scanner. Five compression ratios were used to compress each image, and reconstructed images from each compressed data set were obtained. Altogether, the authors studied 842 images, including the original and compressed-reconstructed images and the images obtained from the difference between the original and the reconstructed images. The results indicate that acceptable compression ratios for 2,048, 1,024, and 512 matrices are 25:1, 20:1, and 10:1, respectively, based on a mean-square error of 0.02%.