Quality Of Compressed Images Research Articles

JPEG2000 Image Compression on Solar EUV Images

For future solar missions as well as ground-based telescopes, efficient ways to return and process data have become increasingly important. Solar Orbiter, e.g., which is the next ESA/NASA mission to explore the Sun and the heliosphere, is a deep-space mission, which implies a limited telemetry rate that makes efficient onboard data compression a necessity to achieve the mission science goals. Missions like the Solar Dynamics Observatory (SDO) and future ground-based telescopes such as the Daniel K. Inouye Solar Telescope, on the other hand, face the challenge of making petabyte-sized solar data archives accessible to the solar community. New image compression standards address these challenges by implementing efficient and flexible compression algorithms that can be tailored to user requirements. We analyse solar images from the Atmospheric Imaging Assembly (AIA) instrument onboard SDO to study the effect of lossy JPEG2000 (from the Joint Photographic Experts Group 2000) image compression at different bit rates. To assess the quality of compressed images, we use the mean structural similarity (MSSIM) index as well as the widely used peak signal-to-noise ratio (PSNR) as metrics and compare the two in the context of solar EUV images. In addition, we perform tests to validate the scientific use of the lossily compressed images by analysing examples of an on-disk and off-limb coronal-loop oscillation time-series observed by AIA/SDO.

Recognizable or Not: Towards Image Semantic Quality Assessment for Compression

Traditionally, image compression was optimized for the pixel-wise fidelity or the perceptual quality of the compressed images given a bit-rate budget. But recently, compressed images are more and more utilized for automatic semantic analysis tasks such as recognition and retrieval. For these tasks, we argue that the optimization target of compression is no longer perceptual quality, but the utility of the compressed images in the given automatic semantic analysis task. Accordingly, we propose to evaluate the quality of the compressed images neither at pixel level nor at perceptual level, but at semantic level. In this paper, we make preliminary efforts towards image semantic quality assessment (ISQA), focusing on the task of optical character recognition (OCR) from compressed images. We propose a full-reference ISQA measure by comparing the features extracted from text regions of original and compressed images. We then propose to integrate the ISQA measure into an image compression scheme. Experimental results show that our proposed ISQA measure is much better than PSNR and SSIM in evaluating the semantic quality of compressed images; accordingly, adopting our ISQA measure to optimize compression for OCR leads to significant bit-rate saving compared to using PSNR or SSIM. Moreover, we perform subjective test about text recognition from compressed images, and observe that our ISQA measure has high consistency with subjective recognizability. Our work explores new dimensions in image quality assessment, and demonstrates promising direction to achieve higher compression ratio for specific semantic analysis tasks.

Performance comparison of JPEG, JPEG 2000, and newly developed CSI‐JPEG by adopting different color models

AbstractThe major issues of using less storage space and wanting higher transmission rates for information in the form of high quality color images was taken into consideration. Two experiments were conducted in order to investigate and compare performance of compression standard including JPEG 1992 and JPEG 2000, and a newly developed CSI‐JPEG. The CSI‐JPEG is an amalgamation of Cubic Spline Interpolation (CSI) with baseline JPEG 1992 algorithm. The performance of different image compression algorithms was evaluated using different color models/spaces in terms of compression rate, color accuracy, and visual quality. The results from three assessment methods consistently showed that JPEG 2000 and CSI‐JPEG performed significantly better compared with JPEG 1992 for small color differences (in the range of acceptability). Moreover, the CAM02‐UCS performed best among other selected models in terms of compression rate and image performance for all three image compression algorithms. The results from the visual assessment also confirmed this. It was also found that CIEDE2000 can be reliably used for assessing quality of compressed images with low levels of distortion. © 2016 Wiley Periodicals, Inc. Col Res Appl, 42, 460–473, 2017

전산화단층촬영 칼라영상의 YIQ모델을 가변블록 이용한 프랙탈 영상 부호화

본 논문에서는 프랙탈 부호화시 변환식의 계수를 찾는 과정에서 블럭의 탐색 영역을 줄이기 위해 탐색 영역인 도메인블록의 특성을 화소의 밝기의 평균에 의한 클래스와 분산에 의한 클래스로 분류하여 리스트를 구성한 후 레인지블록과 같은 클래스를 가지는 도메인블록만 검색하도록 하면서 도메인블럭 탐색시 1 차 허용 오차 한계값을 제어하여 부호화 시간을 향상시켰다. 또한 쿼드트리분할법으로 레인지블록의 크기를 가변시켜 변환( w i )의 수를 줄임으로서 압축효율을 높이고 레인지블록의 크기에 따라 탐색 영역의 탐색 밀도를 변화시켜 화질 개선을 시도하였으며 이러한 영상 기법을 24-bpp 컬러 영상 압축에 적용하였다. 먼저 RGB표색계를 휘도신호와 채도신호를 가지는 YIQ표색계로 변환한 후 영상 정보의 일부분만 차지하고 있는 색의 정보를 나타내는 I,Q신호는 공간평균을 취하여 1/4로 축소하여 부호화하고 복원시에 선형 보간법을 이용하여 다시 원 영상으로 확대하였다. 그 결과 영상의 화질에는 거의 손실이 생기지 않았고 서로 독립성이 강한 RGB영상에 같은 부호화 방법을 사용하였을 때 보다 압축률이나 화질면 에서 우수한 성능을 나타내었다. This paper suggests techniques to enhance coding time which is a problem in traditional fractal compression and to improve fidelity of reconstructed images by determining fractal coefficient through adaptive selection of block approximation formula. First, to reduce coding time, we construct a linear list of domain blocks of which characteristics is given by their luminance and variance and then we control block searching time according to the first permissible threshold value. Next, when employing three-level block partition, if a range block of minimum partition level cannot find a domain block which has a satisfying approximation error, There applied to 24-bpp color image compression and image techniques. The result did not occur a loss in the image quality of the image when using the encoding method, such as almost to the color in the YIQ image compression rate and image quality, such as RGB images and showed good.

Novel application of a quantitative spatial comparison tool to species distribution data

Comparing geographically referenced maps has become an important aspect of spatial ecology (e.g. assessing change in distribution over time). Whilst humans are adept at recognising and extracting structure from maps (i.e. identifying spatial patterns), quantifying these structures can be difficult. Here, we show how the Structural Similarity (SSIM) index, a spatial comparison method adapted from techniques developed in computer science to determine the quality of image compression, can be used to extract additional information from spatial ecological data. We enhance the SSIM index to incorporate uncertainty from the underlying spatial models, and provide a software algorithm to correct for internal edge effects so that loss of spatial information from the map comparison is limited. The SSIM index uses a spatially-local window to calculate statistics based on local mean, variance, and covariance between the maps being compared. A number of statistics can be calculated using the SSIM index, ranging from a single summary statistic to quantify similarities between two maps, to maps of similarities in mean, variance, and covariance that can provide additional insight into underlying biological processes. We demonstrate the applicability of the SSIM approach using a case study of sperm whales in the Mediterranean Sea and identify areas where local-scale differences in space-use between groups and singleton whales occur. We show how novel insights into spatial structure can be extracted, which could not be obtained by visual inspection or cell-by-cell subtraction. As an approach, SSIM is applicable to a broad range of spatial ecological data, providing a novel, implementable tool for map comparison.

COMPARATIVE ANALYSIS: PCA AND JPEG

This paper mainly address the image compression by using Principal component analysis (PCA) and JPEG. Image compression is the method of converting data file into smaller compact files for efficiency of storage and transmission. The main objective of compression of image is to reduce redundancy of bits in the image in order to store and transmit data in an effective way. Image compression techniques deal with the problem of reduction in size of the file which results in efficient storage of image and bandwidth to transmit it. Image Compression helps in storage of more information in a given memory space.PCA is mathematical tool which is used for reducing the dimensionality of data .PCA extracts major variation in the data sets while removing other insignificant components. In this paper PCA is applied for image compression. Since PCA compression is lossy the compressed image is generally degraded as compared to original image. Also PCA is compared with most commonly used image compression technique which is JPEG.JPEG is also a lossy image compression technique which start by separating the image into 8*8 pixel groups and applying DCT transform individual 8*8 pixel groups .In this paper PCA and JPEG compression are applied to the image and various parameter like PSNR and MSE is calculated for both of the compression technique. The Mean Square Error (MSE) and the Peak Signal to Noise Ratio (PSNR) are the two error parameter used to compare image compression quality

Iteration less Wavelet-Fractal Image Compression Applicable in Cellular Mobile Communication System

image compression is a an active area of research with new promising technique that will work very effectively in areas where we have to deal with a huge size of data .In Fractal compression major challenge is the exhaustive comparison needed in the encoding stage. In this paper a method of iteration free- detail space fractal image compression is proposed, in which time of encoding is reduced without compromising much on the quality of image and this algorithm guarantees a high compression ratio. This is a hybrid algorithm of fractal mathematics and wavelet Transform. When comparing with the existing hybrid techniques the advantage of this proposed method is, only the approximation space undergoes exhaustive comparison and thereby it guarantees higher speed than the existing Hybrid techniques. IFS (Iterated function system) of detail space are calculated using the result of approximation space. Experimental results show that in the proposed method Computational over head is considerably reduced and maintains a good tradeoff between compression ratio and quality of image. Resultant image is resolution independent, which is the one of the properties of fractal image compression. Since this new technique guarantees, high compression ratio and low encoding time, it may work very well in mobile communication, especially in face book application.

A Blind Assessment Method of Image Compression Quality Based on Image Variance

Abstract The assessment of image compression result can not only evaluate the quality of image compression results and to a certain extent, can also find the advantages and drawbacks of various compression methods. At the same time, it can provide a reference for the compressed image restoration. Firstly, the classification and shortages of image quality assessment methods are presented. Then, several objective assessment methods usually used for image compression quality are introduced and the recent research progresses are shown. Finally, in view of the shortages of traditional image assessment methods and the existing blind assessment methods, based on image invariance, we propose a blind assessment method of image compression quality by considering the edge detail recovery and artifact removing. Compared with the traditional blind assessment methods, our method is simple in form and evaluation system is easily implemented. The experimental results also show that it is reasonable and effective.

Integrated Data Hiding and Compression Scheme Based on SMVQ and FoE Inpainting

Data Hiding and Compression are equivalently significant terms in the field of communication and its study is inevitable. In this paper, a novel joint data hiding and compression system is ventured based on VQ, SMVQ andimage inpainting. Data hiding and compression scheme are melded into a single unit which will increase the efficiency of communication. As a pre-processing step, the image is divided into non overlapping blocks. The topmost and leftmost blocks are compressed by employing VQ and the residual blocks are embedded with secret data and compressed simultaneously by SMVQ or image inpainting adaptively according to the secret bit to be hidden. VQ is also employed on a threshold basis for some blocks to control the distortion. The concatenated codes received are segmented into indices and secret bits according to the indicator bits. On the receiver side, FoE based inpainting was used for reconstructing lost parts of image. Experimental results validates satisfactory performance in terms of compression ratio and image quality.

Low-Rank Decomposition-Based Restoration of Compressed Images via Adaptive Noise Estimation.

Images coded at low bit rates in real-world applications usually suffer from significant compression noise, which significantly degrades the visual quality. Traditional denoising methods are not suitable for the content-dependent compression noise, which usually assume that noise is independent and with identical distribution. In this paper, we propose a unified framework of content-adaptive estimation and reduction for compression noise via low-rank decomposition of similar image patches. We first formulate the framework of compression noise reduction based upon low-rank decomposition. Compression noises are removed by soft thresholding the singular values in singular value decomposition of every group of similar image patches. For each group of similar patches, the thresholds are adaptively determined according to compression noise levels and singular values. We analyze the relationship of image statistical characteristics in spatial and transform domains, and estimate compression noise level for every group of similar patches from statistics in both domains jointly with quantization steps. Finally, quantization constraint is applied to estimated images to avoid over-smoothing. Extensive experimental results show that the proposed method not only improves the quality of compressed images obviously for post-processing, but are also helpful for computer vision tasks as a pre-processing method.

Considering image structural properties while estimating compressed jpeg image quality

Application of lossy compression methods involves the occurrence of distortions, so the problem of evaluating the level of these distortions is urgent. When using the DCT transformation, the main objective of quality assessment is selecting a statistical distribution model of the DCT coefficients of the image and the methods for estimating the parameters of the model.A universal method of estimating the level of distortions that arise due to JPEG compression of images of any structural content is developed in the paper. To determine the image quality, PSNR metric is used. A key feature of the proposed method lies in involving various statistical models for computing the quantization noise variance of the DCT coefficients. In particular, for models of the DCT coefficients in the form of the double gamma distribution and the generalized Cauchy distribution, calculation-handy expressions for the quantization noise variance of the DCT coefficients of JPEG images are obtained. Using the double gamma distribution to solve the above problem is first proposed.The well-known Laplacian model provides a relatively accurate estimation of PSNR only for those images that mostly consist of regions rich in small parts. While for the images, which contain significant regions of monotonicity, the model in the form of the double gamma distribution provides a much better result.The accuracy of the obtained theoretical expressions is confirmed by the results of experiments with grayscale JPEG images.

Performance Evaluation of JPEG, JPEG2000 and New CSI-JPEG Algorithms by Incorporating Different Color Spaces

In the current work, seven different color spaces were investigated including sRGB, YCbCr, YCoCg-R, RDgDb, CIELAB, IPT, and CAM02-UCS by incorporating into JPEG1992, JPEG2000 and new JPEG. The prime objective of this work was to compare the performance of CSI-JPEG with that of JPEG1992 and JPEG2000. Psychophysical experiments were also performed to examine visual appearance of the compressed and decompressed images. Compression performance was examined for different color spaces by using measures like SNR, PSNR, MSE, CIEDELAB and CIEDE2000. The results showed that CSI-JPEG algorithm provided about 20-30% more compression rate compared to JPEG1992 algorithm for same color difference. Moreover, CAM02-UCS was found to perform best in terms of compression rate and image quality for all three algorithms. The CAM02-UCS was found significantly better compared with linear color spaces but CIELAB, CIELUV, and IPT were insignificantly better. This also implies that it is visually more uniform than CIE uniform color spaces. Results also showed that performance difference of CSI-JPEG and JPEG2000 was not significant but both performed significantly better compared with JPEG1992.

Compressed image quality metric based on perceptually weighted distortion.

Objective quality assessment for compressed images is critical to various image compression systems that are essential in image delivery and storage. Although the mean squared error (MSE) is computationally simple, it may not be accurate to reflect the perceptual quality of compressed images, which is also affected dramatically by the characteristics of human visual system (HVS), such as masking effect. In this paper, an image quality metric (IQM) is proposed based on perceptually weighted distortion in terms of the MSE. To capture the characteristics of HVS, a randomness map is proposed to measure the masking effect and a preprocessing scheme is proposed to simulate the processing that occurs in the initial part of HVS. Since the masking effect highly depends on the structural randomness, the prediction error from neighborhood with a statistical model is used to measure the significance of masking. Meanwhile, the imperceptible signal with high frequency could be removed by preprocessing with low-pass filters. The relation is investigated between the distortions before and after masking effect, and a masking modulation model is proposed to simulate the masking effect after preprocessing. The performance of the proposed IQM is validated on six image databases with various compression distortions. The experimental results show that the proposed algorithm outperforms other benchmark IQMs.

Content-weighted mean-squared error for quality assessment of compressed images

Image quality assessment (IQA) has been intensively studied, especially for the full-reference (FR) scenario. However, only the mean-squared error (MSE) is widely employed in compression. Why other IQA metrics work ineffectively? We first sum up three main limitations including the computational time, portability, and working manner. To address these problems, we then in this paper propose a new content-weighted MSE (CW-MSE) method to assess the quality of compressed images. The design principle of our model is to use adaptive Gaussian convolution to estimate the influence of image content in a block-based manner, thereby to approximate the human visual perception to image quality. Results of experiments on six popular subjective image quality databases (including LIVE, TID2008, CSIQ, IVC, Toyama and TID2013) confirm the superiority of our CW-MSE over state-of-the-art FR IQA approaches.

패턴을 이용한 블록 절단 부호화 기반의 영상 압축

Block Truncation Coding, or BTC, is a type of lossy image compression technique for grayscale images. It divides the original images into blocks and then reduces the number of grey levels in each block to compute the mean and standard deviation. BTC has also been adapted to video compression. Another variation of BTC is Absolute Moment Block Truncation Coding. AMBTC is computationally simpler than BTC. In this paper, we proposed new image compression method based on BTC, which is applied patterns to improve compression rate and image quality. This method make two codebooks to extract 36 and 64 patterns from the highest frequency patterns in BTC. When you are compressing an image, you compare many block patterns to that of codebook and use to compress indexes of identical patterns. We experiment our proposed scheme with 36 patterns and the experimental results showed the compression rate of 1.37 bpp. In this paper, our proposed scheme showed higher compression rate rather than that of BTC. In experiment, we used standard images for the performance evaluation.

Coding algorithm for grayscale images based on Linear Prediction and dual mode quantization

This paper proposes a novel algorithm for grayscale image compression based on dual mode quantization that is supported by improved linear prediction scheme. The idea of dual mode quantization comes from desire to exploit advantages of the both uniform and piecewise uniform quantizers, designed for discrete input samples. The algorithm performs quantizers with a low and medium number of quantization levels and with a fixed codeword length by using a pixel value prediction in preprocessing. The correlation of adjacent pixels is exploited as the main idea for improving the quality of image compression. The proposed prediction is linear and very simple for practical realization. An analysis of reconstructed image quality is presented considering several parameters and by comparing with few other methods – BTC, DPCM and with methods that use transformation coding. Experiments are done applying the proposed compression model to several standard grayscale test images. Special attention is given to determination of thresholds values that determine whether and which of the two offered quantizers to use. Moreover, method for determining the value of proposed quantizer’s variance is explained. Obtained results show that proposed model ensures gain up to 6.14 [dB] compared to the BTC model that uses fixed piecewise uniform quantization for discrete input without a pixel value prediction as well as gain up to 5.89 [dB] compared to the DPCM model that applies dual predictor. The proposed algorithm could find application in current grayscale image compression and video standards.

Hybrid Continuous Wavelet Based Controulet Transform Method for DICOM Image Compression and Improved SPHIT Coding

The widely used standard for medical image storage and transmission is named as Digital Imaging and Communication in Medicine (DICOM). In every field of medicine including diagnosis, treatment, and research, medical images that are obtained as the outputs of the techniques such as the Computerized Tomography (CT), magnetic resonance (MR), digital subtraction angiography (DSA) and Ultrasonography (US) are saved as DICOM format. Network sharing of these larger sized radiology images require large bandwidth. Hence before transferring, compression of such larger image files is necessary for easy and faster communication even with lower bandwidth. Huge amount of data either in multidimensional or multiresolution form is been created as a result of medical information. This makes the following steps like retrieval, efficient storage, management, and transmission of these data a complex process. This complexity could be reduced by compressing the medical data without any loss. Many methods have been proposed so far for compression of the large DICOM images, however with some limitations. Thus, specific methods to overcome the limitations like reducing the noise of MSE error signal and improving the PSNR value results in the medical images are to be proposed for the study. One such method is referred as Hybrid Weibull Probability Density Function based Continuous Wavelet based controulet transform (WPDF-CWBCT) that helps for compression of medical images without any data loss and also for improving the PSNR and reducing the MSE of the signal. The directional filter banks are being applied by initializing using the wavelet transform such that the image coding scheme is maintained based on the proposed transform. WPDF-CWBCT also uses a new set partitioning in hierarchical trees by employing a sorting method (SPIHT) algorithm that provided an embedded code. In this method, the diagnostics capabilities are not compromised to ensure the better performance of image compression is also been justified by the combination of wavelet based controulet transform and SPIHT. The performance evaluation of different DICOM and medical images is possible by using parameters like PSNR, MSE and image compression quality measures.

Impact of Space-to-Ground Video Transmission Constraints on Teleoperated Final Approach and Docking

This study investigates the impact of communication link constraints in the transmission of streaming video feedback on spacecraft teleoperation system performance. A final approach and docking scenario with an uncooperative, rotating target was set up, in which operators in three experiment series were presented with different video frame rate levels, compressed image quality levels, and roundtrip time delay levels. The data show that none of the variables has an effect on the maneuver accuracy, efficiency, or safety. However, there were significant effects on subjective perception and task success. Decreasing the compressed image quality has negligible effect on operator perception, and it has no effect on task success. Reducing the frame rate down to 7.5 framed per second also has no effect, but operator perception dropped significantly with further reduction, without effect on task success. An increase in roundtrip time delay causes a decrease in task success rate between low delays below 750 ms and high delays above 1000 ms, as well as a decrease in subjective system controllability index and an increase in task load. This study thus identifies the envelopes of maximum operator performance for frame rate and roundtrip delay, which translate into communication architecture, power, and frequency requirements.

Efficient Quantization Method for Biometric Fingerprint Image Compression

The choice of quantization method and the requirement to achieve a trade-off between compressed image quality and degradation are very crucial in the overall performance of a lossy image compression algorithm. In this paper, uniform and non-uniform scalar quantization schemes of biometric fingerprint image were studied. Comparative analyses of non-uniform quantization methods were also conducted and these include dither-based quantization and the Lloyd-Max quantization methods. The quality of the quantized output fingerprint image was determined in terms of Signal-to-Quantization Noise Ratio (SQNR). The degree of distortion or quantization error was determined in terms of the Mean Square Quantization Error (MSQE). The non-uniform quantization method performed better than the uniform quantization method in terms of the SQNR and MSQE values. It was also found out that, the performance of dither-based non-uniform quantization on biometric fingerprint image is not as efficient as the LloydMax approach when the number of bits used in the quantization process increased. The results showed that the higher the number of bits used in the quantization process the higher the quality and the less the distortion in the resulting images.

Region-based medical image compression in teleradiology

Digital radiology has resulted in significant increase in use of digital medical images in the process of diagnosis. Lossless image compression is necessary to preserve the value of diagnostic medical images. This method will be applicable to mainly compressing information without any loss. More particularly, this paper aims to increase compression ratio and quality (PSNR) of medical images. Initially, MRI brain image is segmented using grow cut. This algorithm extracts the region of tumour (abnormal region) and non-region of tumour (normal region) parts. The region of tumour is selected using seed point which is selected by extracting run length features. Morphological processing is used for highlighting the tumour region. The integer wavelet transform is applied on both region of tumour and non-region of tumour parts. The abnormal region is compressed losslessly using arithmetic coding and normal region is compressed using lossy compression (i.e.) EZW coding. Finally, the compressed image of the region of tumour (abnormal region) is fused with compressed image of the normal region. This implementation is found to possess superior reconstruction properties and better compression ratio.