Compound document compression with model-based biased reconstruction

The usefulness of electronic document delivery and archives rests in large part on advances in compression technology. Documents can contain complex layouts with different data types, such as text and images, having different statistical characteristics. To achieve better image quality, it is important to make use of such characteristics in compression. We exploit the transform coefficient distributions for text and images. We show that the scheme in base-line JPEG does not lead to minimum mean-square error if we have models of these coefficients. Instead, we discuss an algorithm designed for this performance that involves first classifying the blocks, and then estimating the parameters to enable a biased reconstruction in the decompression value. Simulation results are shown to validate the advantages of this method. © 2004 SPIE and IS&T.published_or_final_versio

Improving mobile color 2D-barcode JPEG image readability using DCT coefficient distributions

Two dimensional (2D) barcodes are becoming a pervasive interface for mobile devices, such as camera smartphones. Often, only monochrome 2D-barcodes are used due to their robustness in an uncontrolled operating environment of smartphones. Nonetheless, we are seeing an emerging use of color 2D-barcodes for camera smartphones. Most smartphones capture and store such 2D-barcode images in the baseline JPEG format. As a lossy compression technique, JPEG does introduce a fair amount of error in the captured 2D-barcode images. In this paper, we analyzed the Discrete Cosine Transform (DCT) coefficient distributions of generalized 2D-barcodes using colored data cells, each comprising of 4, 8 and 10 colors. Using these DCT distributions, we improved the JPEG compression of such mobile barcode images. By altering the JPEG compression parameters based on the DCT coefficient distribution of the barcode images, our improved compression scheme produces JPEG images with higher PSNR value as compared to the baseline implementation. We have also applied our improved scheme to a 10 colors 2D-barcode system; and analyzed its performance in comparison to the default and alternative JPEG schemes. We have found that our improved scheme does provide a marked improvement for the successful decoding of the 10 colors 2D-barcode system

JPEG compression of monochrome 2D-barcode images using DCT coefficient distributions

Two dimensional (2D) barcodes are becoming a pervasive interface for mobile devices, such as camera phones. Often, only monochrome 2D-barcodes are used due to their robustness in an uncontrolled operating environment of camera phones. Most camera phones capture and store such 2D-barcode images in the baseline JPEG format. As a lossy compression technique, JPEG does introduce a fair amount of error in the decoding of captured 2D-barcode images. In this paper, we introduce an improved JPEG compression scheme for such barcode images. By altering the JPEG compression parameters based on the DCT coefficient distribution of such barcode images, the improved compression scheme produces JPEG images with higher PSNR value as compared to the baseline implementation. We have also applied our improved scheme to a real 2D-barcode system - the QR Code and analyzed its performance against the baseline JPEG scheme

Decompression of JPEG Document Images: A Survey Paper

JPEG Decompression techniques are very useful in 3G/4G based markets, handheld devices and infrastructures. There are many challenging issues in previously proposed decompression methods, like very high computational cost, and heavy distortion in ringing and blocking artifacts which makes the image invisible. To improve the visual quality of the JPEG document images at low bit rate and at low computational cost, we are going to implement the decompression technique for JPEG document images. We first divide the JPEG document image into smooth and non-smooth blocks with the help of Discrete Cosine Transform (DCT). Then the smooth blocks (background , uniform region) are decoded in the transform domain by minimizing the Total Block Boundary Variation(TBBV). In this we propose to compute the block variation directly in the DCT domain at the super pixel level. The super pixel have size n*n, each super pixel is assigned with an average intensity value. The smooth blocks are then reconstructed by using the Newton’s method. The implementation of the smooth block decompression will be done here. The non-smooth blocks of the document image contains the text and graphics/line drawing objects. The post processing algorithm will be introduced which takes into consideration the specificities of document content. The inverse DCT is applied to represent the image in spatial domain. So the implementation of the non-smooth block decompression will be done here. Finally, we design different experimental results and analyze that our system is better than the existing. And it will show the quality improvement of decompressed JPEG document image

Processing of natural temporal stimuli by macaque retinal ganglion cells

This study quantifies the performance of primate retinal ganglion cells in response to natural stimuli. Stimuli were confined to the temporal and chromatic domains and were derived from two contrasting environments, one typically northern European and the other a flower show. The performance of the cells was evaluated by investigating variability of cell responses to repeated stimulus presentations and by comparing measured to model responses. Both analyses yielded a quantity called the coherence rate (in bits per second), which is related to the information rate. Magnocellular (MC) cells yielded coherence rates of up to 100 bits/sec, rates of parvocellular (PC) cells were much lower, and short wavelength (S)-cone-driven ganglion cells yielded intermediate rates. The modeling approach showed that for MC cells, coherence rates were generated almost exclusively by the luminance content of the stimulus. Coherence rates of PC cells were also dominated by achromatic content. This is a consequence of the stimulus structure; luminance varied much more in the natural environment than chromaticity. Only approximately one-sixth of the coherence rate of the PC cells derived from chromatic content, and it was dominated by frequencies below 10 Hz. S-cone-driven ganglion cells also yielded coherence rates dominated by low frequencies. Below 2–3 Hz, PC cell signals contained more power than those of MC cells. Response variation between individual ganglion cells of a particular class was analyzed by constructing generic cells, the properties of which may be relevant for performance higher in the visual system. The approach used here helps define retinal modules useful for studies of higher visual processing of natural stimuli

Generative adversarial networks review in earthquake-related engineering fields

Within seismology, geology, civil and structural engineering, deep learning (DL), especially via generative adversarial networks (GANs), represents an innovative, engaging, and advantageous way to generate reliable synthetic data that represent actual samples' characteristics, providing a handy data augmentation tool. Indeed, in many practical applications, obtaining a significant number of high-quality information is demanding. Data augmentation is generally based on artificial intelligence (AI) and machine learning data-driven models. The DL GAN-based data augmentation approach for generating synthetic seismic signals revolutionized the current data augmentation paradigm. This study delivers a critical state-of-art review, explaining recent research into AI-based GAN synthetic generation of ground motion signals or seismic events, and also with a comprehensive insight into seismic-related geophysical studies. This study may be relevant, especially for the earth and planetary science, geology and seismology, oil and gas exploration, and on the other hand for assessing the seismic response of buildings and infrastructures, seismic detection tasks, and general structural and civil engineering applications. Furthermore, highlighting the strengths and limitations of the current studies on adversarial learning applied to seismology may help to guide research efforts in the next future toward the most promising directions

Deep-Learning-Driven Techniques for Real-Time Multimodal Health and Physical Data Synthesis

With the advent of Artificial Intelligence for healthcare, data synthesis methods present crucial benefits in facilitating the fast development of AI models while protecting data subjects and bypassing the need to engage with the complexity of data sharing and processing agreements. Existing technologies focus on synthesising real-time physiological and physical records based on regular time intervals. Real health data are, however, characterised by irregularities and multimodal variables that are still hard to reproduce, preserving the correlation across time and different dimensions. This paper presents two novel techniques for synthetic data generation of real-time multimodal electronic health and physical records, (a) the Temporally Correlated Multimodal Generative Adversarial Network and (b) the Document Sequence Generator. The paper illustrates the need and use of these techniques through a real use case, the H2020 GATEKEEPER project of AI for healthcare. Furthermore, the paper presents the evaluation for both individual cases and a discussion about the comparability between techniques and their potential applications of synthetic data at the different stages of the software development life-cycle

Sparse Modeling for Image and Vision Processing

In recent years, a large amount of multi-disciplinary research has been conducted on sparse models and their applications. In statistics and machine learning, the sparsity principle is used to perform model selection---that is, automatically selecting a simple model among a large collection of them. In signal processing, sparse coding consists of representing data with linear combinations of a few dictionary elements. Subsequently, the corresponding tools have been widely adopted by several scientific communities such as neuroscience, bioinformatics, or computer vision. The goal of this monograph is to offer a self-contained view of sparse modeling for visual recognition and image processing. More specifically, we focus on applications where the dictionary is learned and adapted to data, yielding a compact representation that has been successful in various contexts.Comment: 205 pages, to appear in Foundations and Trends in Computer Graphics and Visio