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Practical real-time image compression for resource-challenged devices

Practical real-time image compression for resource-challenge...

引用

conference on multimodal image exploitation and learning

作者： Pham, Kevin Depoian, Arthur C., II Bailey, Colleen P. Univ North Texas Dept Elect Engn Denton TX 76207 USA

ISBN: (纸本)9781510673854;9781510673847

In the era of rapidly expanding image data, the demand for improved image compression algorithms has grown significantly, particularly with the integration of deep learning approaches into traditional image processing tasks. However, many of the existing solutions in this domain are burdened by computational complexity, rendering them unsuitable for real-time deployment on standard devices as they often necessitate complex systems and substantial energy consumption. This work addresses the growing paradigm of edge computing for real-time applications by introducing a novel, on-edge device solution. This innovative approach aims to strike a balance between efficiency and accuracy, adhering to the practical constraints of real-world deployment. By presenting demonstrations of the proposed solution's performance on readily available devices, we provide tangible evidence of its applicability and viability in real-world scenarios. This advance contributes to the ongoing dialogue about the need for accessible and efficient image compression algorithms that can be deployed real-time applications on edge devices, bridging the gap between the demanding computational requirements of deep learning and the practical limitations of everyday hardware. As data continues to surge, solutions like this become ever more critical in ensuring effective image compression, aligning with on-edge computing within AI. This research paves the way for improved image processing in real-time applications while conserving computational resources and energy consumption.

关键词： image compression efficient AI real time processing

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image Registration Refinement Using Progressively Sharpened Alpha-Rooting Parameters

Image Registration Refinement Using Progressively Sharpened ...

引用

conference on multimodal image exploitation and learning

作者： DelMarco, Stephen BAE Syst 600 Dist Ave Burlington MA 01803 USA

ISBN: (纸本)9781510673854;9781510673847

image exploitation algorithms often require image registration to align imagery prior to processing. For example, for image fusion processing, image alignment enables consistent extraction of corresponding features. For geo-location applications, mission imagery, acquired by an onboard camera, properly-aligned with geo-located reference imagery enables extraction of geo-coordinates from pixels-of-interest. Geo-coordinates of candidate target or landmark pixels result from association with geo-located reference pixels via the image alignment. image correlation represents a standard, classic form of image matching and image registration, which has been adapted and modified in several ways over the years. One modification consists of applying alpha-rooting to the Fourier domain image magnitudes to implement tunable Fourier domain image whitening. The whitening process emphasizes the phase content, which contains much of the image information, and mitigates effects from amplitude variations due to changing illumination conditions. Choice of alpha-rooting parameters provides the capability to adapt the whitening characteristics to the properties of the imagery, and the application in use. In this paper, we present an iterative image registration algorithm that exploits the shape properties of a region of the peak correlation coefficient, under progressive alpha-rooting parameter sharpening. Together with a coarse grid search approach, we converge to the correct image registration solution, while reducing computational load relative to a high resolution parameter space search. We provide a brief review of alpha-rooted phase correlation and describe the technical formulation. We present numerical results showing the effectiveness of the approach relative to standard correlation approaches.

关键词： image registration coarse grid hierarchical grid alpha-rooting correlation

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Adaptive Inertia Weight Particle Swarm Algorithm for Optimized Hyperspectral image Enhancement

Adaptive Inertia Weight Particle Swarm Algorithm for Optimiz...

引用

conference on multimodal image exploitation and learning

作者： Trongtirakul, Thaweesak Agaian, Sos Raj Amangala Univ Technol Phra Nakhon Fac Ind Educ 399 Samsen Rd Bangkok 10300 Thailand CUNY Coll Staten Isl Dept Comp Sci 2800 Victory Blvd Staten Isl NY 10314 USA

ISBN: (数字)9781510643062

ISBN: (纸本)9781510643062

Hyperspectral images deliver several hundred spectral bands covering the visible and the infrared wavelength. Visualize hyperspectral information on a trichromatic display is impracticable to show information, which contains a hundred bands. Therefore, the selection of representative bands and the improvement of image quality are challenging tasks. In this paper, a simple, effective hyperspectral image visualization method based on hyperspectral image enhancement with the use of different cost functions is proposed. The proposed method consists of two major steps. First, the wavelength-based band selection chooses three subsets of adjacent hyperspectral bands. Second, the selected bands are improved by the proposed fractional contrast stretching algorithm with optimizations. In the nature-inspired optimization algorithm with an adaptive inertia weight, the quality of an enhanced image will be evaluated by different cost functions - image quality measures. This paper's main contribution is that: i) the selection of representative bands corresponding to natural-color appearance, ii) the image enhancement for visualization, and iii) the investigation of the suitable cost function for hyperspectral image visualization. Experiments performed on several hyperspectral datasets illustrate that the proposed method can produce remarkable visualization performance in subjective and objective assessments.

关键词： Hyperspectral image Enhancement Hyperspectral image Visualization Wavelength-based Band Selection

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Modification of the method of multicriteria image processing for preprocessing thermal imaging images with adaptive change in filtering parameters based on neural network approaches

Modification of the method of multicriteria image processing...

引用

conference on multimodal image exploitation and learning

作者： Semenishchev, Evgeny Agaian, Sos Zelensky, Aleksandr Surkov, Evgenii Khamidullin, Ilia Voronin, Viacheslav Tula State Univ Tu1SU 92 Sq Lenina Tula 300012 Tula Region Russia CUNY Dept Comp Sci New York NY 10314 USA Moscow State Tech Univ STANKIN 1a Vadkovsky Moscow 127055 Russia

ISBN: (纸本)9781510650770;9781510650763

The article presents a noise reduction method based on minimizing a multicriteria objective function. The technique makes it possible to perform minimization according to the criteria of the root-mean-square difference of the deviation between adjacent estimates of pixel values (vertical, horizontal) and between the mean-square difference of the input elements and the resulting estimates. The first criterion allows you to reduce the noise component in locally stationary areas of the image, the second to preserve the boundaries of transitions between objects. In the article, the adaptation of the choice of the processing parameter is performed using a trained neural network. The training was carried out on standard test images from widely used databases (Kodak, MS COCO, etc.). Tables comparing the effectiveness of the proposed adaptation algorithm to the previously applied approach are given.

关键词： denoising image adaptive parameterization image filtering

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Joint image Enhancement and Localization Framework for Vehicle Model Recognition in the Presence of Non-Uniform Lighting Conditions

Joint Image Enhancement and Localization Framework for Vehic...

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conference on multimodal image exploitation and learning

作者： Kezebou, Landry Oludare, Victor Panetta, Karen Agaian, Sos Tufts Univ Medford MA 02155 USA CUNY New York NY 10021 USA

ISBN: (数字)9781510643062

ISBN: (纸本)9781510643062

Recognizing the model of a vehicle in natural scene images is an important and challenging task for real-life applications. Current methods perform well under controlled conditions, such as frontal and horizontal view-angles or under optimal lighting conditions. Nevertheless, their performance decreases significantly in an unconstrained environment, that may include extreme darkness or over illuminated conditions. Other challenges to recognition systems include input images displaying very low visual quality or considerably low exposure levels. This paper strives to improve vehicle model recognition accuracy in dark scenes by using a deep neural network model. To boost the recognition performance of vehicle models, the approach performs joint enhancement and localization of vehicles for non-uniform-lighting conditions. Experimental results on several public datasets demonstrate the generality and robustness of our framework. It improves vehicle detection rate under poor lighting conditions, localizes objects of interest, and yields better vehicle model recognition accuracy on low-quality input image data.

关键词： image enhancement vehicle model and make recognition object detection deep learning object recognition in the dark vehicle detection and recognition in the dark

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Thermal image Enhancement by Artificial Multiscale-Exposure image Fusion

Thermal Image Enhancement by Artificial Multiscale-Exposure ...

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conference on multimodal image exploitation and learning

作者： Voronin, V. Gapon, N. Zhdanova, M. Semenishchev, E. Moscow State Univ Technol STANKIN Ctr Cognit Technol & Machine Vis Moscow Russia Don State Tech Univ Rostov Na Donu Russia

ISBN: (纸本)9781510673854;9781510673847

Infrared and thermal images have been used widely in different security applications. One of the drawbacks of such images is low contrast and noisy images, which should be enhanced. We present a new image enhancement algorithm based on block-rooting processing with artificial multi-scale-exposure image fusion. The proposed block-based multi-scale enhancement method is based on a 3-D block-rooting transform domain technique comprised: finding similar blocks in the image by block-matching;block-grouping for different block sizes;applying 3-D block-matching image enhancement;decomposition of the weight map and multi-scale enhanced images into the Gaussian and Laplacian pyramids;fusion by multiplying multi-scale images and weights. A new stage is proposed to obtain a local-global estimate of high-contrast images, also used in the general artificial fusion model. Some presented experimental results illustrate the performance of the proposed method on the thermal image dataset compared with the traditional methods.

关键词： image enhancement thermal imaging multi-scale processing frequency-domain transform multi-exposure fusion

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Cybersecurity Enhancement Using Recurrent Neural Networks and Keystroke Dynamics

Cybersecurity Enhancement Using Recurrent Neural Networks an...

引用

conference on multimodal image exploitation and learning

作者： Zheng, Yufeng Elmaghraby, Adel S. Univ Mississippi Med Ctr Dept Data Sci Jackson MS 39216 USA Univ Louisville Dept Comp Sci & Engn Louisville KY 40292 USA

ISBN: (数字)9781510643062

ISBN: (纸本)9781510643062

Three-factor authentication is the best available option for cybersecurity enhancement, which includes 'know' (password), 'have' (username, token, or card) and 'are' (biometrics). Each one makes this process stronger and more secure. Imagine an international team working on a long-term project by remotely logging into a secured server. In such a context, adding keystroke biometrics to authentication will definitely improves the cybersecurity. We propose to develop a set of recurrent neural network (RNN) models of utilizing keystroke dynamics as ones' biometrics to enhance cybersecurity. Keystroke dynamics refers to the process of measuring and assessing human's typing rhythm on digital devices. Keystroke timing information such as di-graph, dwell time and flight time are used in our experimental datasets. We propose to apply support vector machine and recurrent neural network to keystroke dynamics. Experimental results show the proposed methods are promising in contrast with traditional methods like nearest neighbor and Manhattan distance.

关键词： Cybersecurity enhancement Keystroke biometrics Recurrent neural network (RNN) Deep learning

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Multibit Least Significant Bit Matching: A Novel Approach to image Steganography

Multibit Least Significant Bit Matching: A Novel Approach to...

引用

conference on multimodal image exploitation and learning

作者： Essa, Almabrok Wu, Ian John Carroll Univ Dept Math Comp Sci & Data Sci University Hts OH 44118 USA Cleveland State Univ Dept Elect Engn & Comp Sci Cleveland OH USA

ISBN: (纸本)9781510673854;9781510673847

Steganography has a large range of potential applications, particularly with the advent of the internet and intellectual property concerns. A particular technique, Least Significant Bit (LSB) Steganography, is commonly used for image-in-image steganography. However, LSB steganography is a bit weak against steganalysis attacks that aim to detect the presence of embedded data. This weakness has been improved upon in Least Significant Bit Matching (LSBM) Steganography, which attempts to preserve the underlying structure of the image by maintaining a similar number of 1s and 0s as in the original image. However, standard LSBM steganography is only able to encode 1 bit of information per channel per pixel, which limits the information that could be embedded. To this end, a novel multibit approach to LSBM steganography is proposed named Multibit Least Significant Bit Matching (MLSBM) Steganography. The MLSBM approach preserves the underlying structure of the image, while allowing multiple bits to be encoded of each pixel in each channel. In addition, when the proposed MLSBM technique is used to embed high number of bits, it significantly reduces the visual perceptibility of the embedding when it is compared with the other embedding techniques for the same number of bits.

关键词： LSB LSBM MLSBM Steganography Steganalysis

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Quantifying the Impact of Complementary Visual and Textual Cues Under image Captioning

Quantifying the Impact of Complementary Visual and Textual C...

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IEEE International conference on Systems, Man, and Cybernetics (SMC)

作者： Akilan, Thangarajah Thiagarajan, Amitha Venkatesan, Bharathwaaj Thirumeni, Sowmiya Chandrasekaran, Sanjana Gurusamy Lakehead Univ Dept Software Engn Thunder Bay ON Canada Lakehead Univ Dept Comp Sci Thunder Bay ON Canada

ISBN: (纸本)9781728185262

Describing an image with natural sentence without human involvement requires knowledge of both image processing and Natural Language Processing (NLP). Most of the existing works are based on unimodal representations of the visual and textual contents using an Encoder-Decoder (EnDec) Deep Neural Network (DNN), where the input images are encoded using Convolutional Neural Network (CNN) and the caption is generated by a Recurrent Neural Network (RNN). This paper dives into a basic image captioning model to quantify the impact of multimodal representation of the visual and textual cues. The multimodal representation is carried out via an early fusion of encoded visual cues from different CNNs, along with combined textual features from different word embedding techniques. The resultant of the multimodal representation of the visual and textual cues are employed to train a Long Short-Term Memory (LSTM)-based baseline caption generator to quantify the impact of various levels of complementary feature mutations. The ablation study involves two different CNN feature extractors and two types of textual feature extractors, shows that exploitation of the complementary information outperforms the unimodal representations significantly with endurable timing overhead.

关键词： image captioning multimodal learning NLP

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An Impact Study of Deep learning-based Low-light image Enhancement in Intelligent Transportation Systems

An Impact Study of Deep Learning-based Low-light Image Enhan...

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conference on multimodal image exploitation and learning

作者： Jinadu, Obafemi Rajeev, Srijith Panetta, Karen A. Agaian, Sos S. Tufts Univ Dept Elect & Comp Engn Medford MA 02155 USA City Univ New York Dept Comp Sci New York NY USA

ISBN: (纸本)9781510673854;9781510673847

images and videos captured in poor illumination conditions are degraded by low brightness, reduced contrast, color distortion, and noise, rendering them barely discernable for human perception and ultimately negatively impacting computer vision system performance. These challenges are exasperated when processing video surveillance camera footage, using this unprocessed video data as-is for real-time computer vision tasks across varying environmental conditions within Intelligent Transportation Systems (ITS), such as vehicle detection, tracking, and timely incident detection. The inadequate performance of these algorithms in real-world deployments incurs significant operational costs. Low-light image enhancement (LLIE) aims to improve the quality of images captured in these unideal conditions. Groundbreaking advancements in LLIE have been recorded employing deep-learning techniques to address these challenges, however, the plethora of models and approaches is varied and disparate. This paper presents an exhaustive survey to explore a methodical taxonomy of state-of-the-art deep learning-based LLIE algorithms and their impact when used in tandem with other computer vision algorithms, particularly detection algorithms. To thoroughly evaluate these LLIE models, a subset of the BDD100K dataset, a diverse real-world driving dataset is used for suitable image quality assessment and evaluation metrics. This study aims to provide a detailed understanding of the dynamics between low-light image enhancement and ITS performance, offering insights into both the technological advancements in LLIE and their practical implications in real-world conditions. The project Github repository can be accessed here.

关键词： low-light image enhancement deep learning image restoration computer vision

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