Accelerating BPC-PaCo through visually lossless techniques

Fast image codecs are a current need in applications that deal with large amounts of images. Graphics Processing Units (GPUs) are suitable processors to speed up most kinds of algorithms, especially when they allow fine-grain parallelism. Bitplane Coding with Parallel Coefficient processing (BPC-PaCo) is a recently proposed algorithm for the core stage of wavelet-based image codecs tailored for the highly parallel architectures of GPUs. This algorithm provides complexity scalability to allow faster execution at the expense of coding efficiency. Its main drawback is that the speedup and loss in image quality is controlled only roughly, resulting in visible distortion at low and medium rates. This paper addresses this issue by integrating techniques of visually lossless coding into BPC-PaCo. The resulting method minimizes the visual distortion introduced in the compressed file, obtaining higher-quality images to a human observer. Experimental results also indicate 12% speedups with respect to BPC-PaCo

No-Reference Quality Assessment of digital images

Ph.DDOCTOR OF PHILOSOPH

The quest for "diagnostically lossless" medical image compression using objective image quality measures

Given the explosive growth of digital image data being generated, medical communities worldwide have recognized the need for increasingly efficient methods of storage, display and transmission of medical images. For this reason lossy image compression is inevitable. Furthermore, it is absolutely essential to be able to determine the degree to which a medical image can be compressed before its “diagnostic quality” is compromised. This work aims to achieve “diagnostically lossless compression”, i.e., compression with no loss in visual quality nor diagnostic accuracy. Recent research by Koff et al. has shown that at higher compression levels lossy JPEG is more effective than JPEG2000 in some cases of brain and abdominal CT images. We have investigated the effects of the sharp skull edges in CT neuro images on JPEG and JPEG 2000 lossy compression. We provide an explanation why JPEG performs better than JPEG2000 for certain types of CT images. Another aspect of this study is primarily concerned with improved methods of assessing the diagnostic quality of compressed medical images. In this study, we have compared the performances of structural similarity (SSIM) index, mean squared error (MSE), compression ratio and JPEG quality factor, based on the data collected in a subjective experiment involving radiologists. An receiver operating characteristic (ROC) curve and Kolmogorov-Smirnov analyses indicate that compression ratio is not always a good indicator of visual quality. Moreover, SSIM demonstrates the best performance. We have also shown that a weighted Youden index can provide SSIM and MSE thresholds for acceptable compression. We have also proposed two approaches of modifying L2-based approximations so that they conform to Weber’s model of perception. We show that the imposition of a condition of perceptual invariance in greyscale space according to Weber’s model leads to the unique (unnormalized) measure with density function ρ(t) = 1/t. This result implies that the logarithmic L1 distance is the most natural “Weberized” image metric. We provide numerical implementations of the intensity-weighted approximation methods for natural and medical images

Remote Sensing Data Compression

A huge amount of data is acquired nowadays by different remote sensing systems installed on satellites, aircrafts, and UAV. The acquired data then have to be transferred to image processing centres, stored and/or delivered to customers. In restricted scenarios, data compression is strongly desired or necessary. A wide diversity of coding methods can be used, depending on the requirements and their priority. In addition, the types and properties of images differ a lot, thus, practical implementation aspects have to be taken into account. The Special Issue paper collection taken as basis of this book touches on all of the aforementioned items to some degree, giving the reader an opportunity to learn about recent developments and research directions in the field of image compression. In particular, lossless and near-lossless compression of multi- and hyperspectral images still remains current, since such images constitute data arrays that are of extremely large size with rich information that can be retrieved from them for various applications. Another important aspect is the impact of lossless compression on image classification and segmentation, where a reasonable compromise between the characteristics of compression and the final tasks of data processing has to be achieved. The problems of data transition from UAV-based acquisition platforms, as well as the use of FPGA and neural networks, have become very important. Finally, attempts to apply compressive sensing approaches in remote sensing image processing with positive outcomes are observed. We hope that readers will find our book useful and interestin

Metrics for Stereoscopic Image Compression

Metrics for automatically predicting the compression settings for stereoscopic images, to minimize file size, while still maintaining an acceptable level of image quality are investigated. This research evaluates whether symmetric or asymmetric compression produces a better quality of stereoscopic image. Initially, how Peak Signal to Noise Ratio (PSNR) measures the quality of varyingly compressed stereoscopic image pairs was investigated. Two trials with human subjects, following the ITU-R BT.500-11 Double Stimulus Continuous Quality Scale (DSCQS) were undertaken to measure the quality of symmetric and asymmetric stereoscopic image compression. Computational models of the Human Visual System (HVS) were then investigated and a new stereoscopic image quality metric designed and implemented. The metric point matches regions of high spatial frequency between the left and right views of the stereo pair and accounts for HVS sensitivity to contrast and luminance changes in these regions. The PSNR results show that symmetric, as opposed to asymmetric stereo image compression, produces significantly better results. The human factors trial suggested that in general, symmetric compression of stereoscopic images should be used. The new metric, Stereo Band Limited Contrast, has been demonstrated as a better predictor of human image quality preference than PSNR and can be used to predict a perceptual threshold level for stereoscopic image compression. The threshold is the maximum compression that can be applied without the perceived image quality being altered. Overall, it is concluded that, symmetric, as opposed to asymmetric stereo image encoding, should be used for stereoscopic image compression. As PSNR measures of image quality are correctly criticized for correlating poorly with perceived visual quality, the new HVS based metric was developed. This metric produces a useful threshold to provide a practical starting point to decide the level of compression to use

Investigating Polynomial Fitting Schemes for Image Compression

Image compression is a means to perform transmission or storage of visual data in the most economical way. Though many algorithms have been reported, research is still needed to cope with the continuous demand for more efficient transmission or storage. This research work explores and implements polynomial fitting techniques as means to perform block-based lossy image compression. In an attempt to investigate nonpolynomial models, a region-based scheme is implemented to fit the whole image using bell-shaped functions. The idea is simply to view an image as a 3D geographical map consisting of hills and valleys. However, the scheme suffers from high computational demands and inferiority to many available image compression schemes. Hence, only polynomial models get further considerations. A first order polynomial (plane) model is designed to work in a multiplication- and division-free (MDF) environment. The intensity values of each image block are fitted to a plane and the parameters are then quantized and coded. Blocking artefacts, a common drawback of block-based image compression techniques, are reduced using an MDF line-fitting scheme at blocks’ boundaries. It is shown that a compression ratio of 62:1 at 28.8dB is attainable for the standard image PEPPER, outperforming JPEG, both objectively and subjectively for this part of the rate-distortion characteristics. Inter-block prediction can substantially improve the compression performance of the plane model to reach a compression ratio of 112:1 at 27.9dB. This improvement, however, slightly increases computational complexity and reduces pipelining capability. Although JPEG2000 is not a block-based scheme, it is encouraging that the proposed prediction scheme performs better in comparison to JPEG 2000, computationally and qualitatively. However, more experiments are needed to have a more concrete comparison. To reduce blocking artefacts, a new postprocessing scheme, based on Weber’s law, is employed. It is reported that images postprocessed using this scheme are subjectively more pleasing with a marginal increase in PSNR (<0.3 dB). The Weber’s law is modified to perform edge detection and quality assessment tasks. These results motivate the exploration of higher order polynomials, using three parameters to maintain comparable compression performance. To investigate the impact of higher order polynomials, through an approximate asymptotic behaviour, a novel linear mapping scheme is designed. Though computationally demanding, the performances of higher order polynomial approximation schemes are comparable to that of the plane model. This clearly demonstrates the powerful approximation capability of the plane model. As such, the proposed linear mapping scheme constitutes a new approach in image modeling, and hence worth future consideration

Study of machine learning techniques for image compression

In the age of the Internet and cloud-based applications, image compression has become increasingly important. Moreover, image processing has recently sparked the interest of technology companies as autonomous machines powered by artificial intelligence using images as input are rapidly growing. Reducing the amount of information needed to represent an image is key to reducing the amount of storage space, transmission bandwidth, and computation time required to process the image, which in turn saves resources, energy, and money. This study aims to investigate machine learning techniques (Fourier, wavelets, and PCA) for image compression. Several Fourier and wavelet methods are included, such as the wellknown Cooley-Tukey algorithm, the discrete cosine transform, and the Mallart algorithm, among others. To comprehend each step of image compression, an object-oriented Matlab code has been developed in-house. To do so, extensive research in machine learning techniques, not only in terms of theoretical understanding, but also in the mathematics that support it. The developed code is used to compare the performance of the different compression techniques studied. The findings of this study are consistent with the advances in image compression technologies in recent years, with the dominance of the JPEG compression method (Fourier) and later JPEG2000 (wavelets) reigning supreme