Improved image analysis methodology for detecting changes in evidence positioning at crime scenes
- Authors: Petty, Mark , Teng, Shyh , Murshed, Manzur
- Date: 2019
- Type: Text , Conference proceedings , Conference paper
- Relation: 2019 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2019
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- Description: This paper proposed an improved methodology to assist forensic investigators in detecting positional change of objects due to crime scene contamination. Either intentionally or by accident, crime scene contamination can occur during the investigation and documentation process. This new proposed methodology utilises an ASIFT-based feature detection algorithm that compares pre- and post-contaminated images of the same scene, taken from different viewpoints. The contention is that the ASIFT registration technique is better suited to real world crime scene photography, being more robust to affine distortion that occurs when capturing images from different viewpoints. The proposed methodology was tested with both the SIFT and ASIFT registration techniques to show that (1) it could identify missing, planted and displaced objects using both SIFT and ASIFT and (2) ASIFT is superior to SIFT in terms of error in displacement estimation, especially for larger viewpoint discrepancies between the pre- and post-contamination images. This supports the contention that our proposed methodology in combination with ASIFT is better suited to handle real world crime scene photography. © 2019 IEEE.
- Description: E1
Measuring trustworthiness of IoT image sensor data using other sensors' complementary multimodal data
- Authors: Islam, Mohammad , Karmakar, Gour , Kamruzzaman, Joarder , Murshed, Manzur
- Date: 2019
- Type: Text , Conference proceedings , Conference paper
- Relation: 18th IEEE International Conference on Trust, Security and Privacy in Computing and Communications/13th IEEE International Conference on Big Data Science and Engineering, TrustCom/BigDataSE 2019 p. 775-780
- Full Text: false
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- Description: Trust of image sensor data is becoming increasingly important as the Internet of Things (IoT) applications grow from home appliances to surveillance. Up to our knowledge, there exists only one work in literature that estimates trustworthiness of digital images applied to forensic applications, based on a machine learning technique. The efficacy of this technique is heavily dependent on availability of an appropriate training set and adequate variation of IoT sensor data with noise, interference and environmental condition, but availability of such data cannot be assured always. Therefore, to overcome this limitation, a robust method capable of estimating trustworthy measure with high accuracy is needed. Lowering cost of sensors allow many IoT applications to use multiple types of sensors to observe the same event. In such cases, complementary multimodal data of one sensor can be exploited to measure trust level of another sensor data. In this paper, for the first time, we introduce a completely new approach to estimate the trustworthiness of an image sensor data using another sensor's numerical data. We develop a theoretical model using the Dempster-Shafer theory (DST) framework. The efficacy of the proposed model in estimating trust level of an image sensor data is analyzed by observing a fire event using IoT image and temperature sensor data in a residential setup under different scenarios. The proposed model produces highly accurate trust level in all scenarios with authentic and forged image data. © 2019 IEEE.
- Description: E1
A novel no-reference subjective quality metric for free viewpoint video using human eye movement
- Authors: Podder, Pallab , Paul, Manoranjan , Murshed, Manzur
- Date: 2018
- Type: Text , Conference proceedings
- Relation: 8th Pacific-Rim Symposium on Image and Video Technology, PSIVT 2017; Wuhan, China; 20th-24th November 2017; published in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 10749 LNCS, p. 237-251
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- Description: The free viewpoint video (FVV) allows users to interactively control the viewpoint and generate new views of a dynamic scene from any 3D position for better 3D visual experience with depth perception. Multiview video coding exploits both texture and depth video information from various angles to encode a number of views to facilitate FVV. The usual practice for the single view or multiview quality assessment is characterized by evolving the objective quality assessment metrics due to their simplicity and real time applications such as the peak signal-to-noise ratio (PSNR) or the structural similarity index (SSIM). However, the PSNR or SSIM requires reference image for quality evaluation and could not be successfully employed in FVV as the new view in FVV does not have any reference view to compare with. Conversely, the widely used subjective estimator- mean opinion score (MOS) is often biased by the testing environment, viewers mode, domain knowledge, and many other factors that may actively influence on actual assessment. To address this limitation, in this work, we devise a no-reference subjective quality assessment metric by simply exploiting the pattern of human eye browsing on FVV. Over different quality contents of FVV, the participants eye-tracker recorded spatio-temporal gaze-data indicate more concentrated eye-traversing approach for relatively better quality. Thus, we calculate the Length, Angle, Pupil-size, and Gaze-duration features from the recorded gaze trajectory. The content and resolution invariant operation is carried out prior to synthesizing them using an adaptive weighted function to develop a new quality metric using eye traversal (QMET). Tested results reveal that the proposed QMET performs better than the SSIM and MOS in terms of assessing different aspects of coded video quality for a wide range of FVV contents.
- Description: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Cuboid colour image segmentation using intuitive distance measure
- Authors: Tania, Sheikh , Murshed, Manzur , Teng, Shyh , Karmakar, Gour
- Date: 2018
- Type: Text , Conference proceedings
- Relation: 2018 International Conference on Image and Vision Computing New Zealand, IVCNZ 2018; Auckland, New Zealand; 19th-21st November 2018 Vol. 2018-November, p. 1-6
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- Description: In this paper, an improved algorithm for cuboid image segmentation is proposed. To address the two main limitations of the recently proposed cuboid segmentation algorithm, the improved algorithm substitutes colour quantization in HCL colour space with infinity norm distance in RGB colour space along with a different way to impose area thresholding. We also propose a new metric to evaluate the quality of segmentation. Experimental results show that the proposed cuboid segmentation algorithm significantly outperforms the existing cuboid segmentation algorithm in terms of quality of segmentation.
- Description: International Conference Image and Vision Computing New Zealand
Detecting splicing and copy-move attacks in color images
- Authors: Islam, Mohammad , Karmakar, Gour , Kamruzzaman, Joarder , Murshed, Manzur , Kahandawa, Gayan , Parvin, Nahida
- Date: 2018
- Type: Text , Conference proceedings
- Relation: 2018 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2018; Canberra, Australia; 10th-13th December 2018 p. 1-7
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- Description: Image sensors are generating limitless digital images every day. Image forgery like splicing and copy-move are very common type of attacks that are easy to execute using sophisticated photo editing tools. As a result, digital forensics has attracted much attention to identify such tampering on digital images. In this paper, a passive (blind) image tampering identification method based on Discrete Cosine Transformation (DCT) and Local Binary Pattern (LBP) has been proposed. First, the chroma components of an image is divided into fixed sized non-overlapping blocks and 2D block DCT is applied to identify the changes due to forgery in local frequency distribution of the image. Then a texture descriptor, LBP is applied on the magnitude component of the 2D-DCT array to enhance the artifacts introduced by the tampering operation. The resulting LBP image is again divided into non-overlapping blocks. Finally, summations of corresponding inter-cell values of all the LBP blocks are computed and arranged as a feature vector. These features are fed into a Support Vector Machine (SVM) with Radial Basis Function (RBF) as kernel to distinguish forged images from authentic ones. The proposed method has been experimented extensively on three publicly available well-known image splicing and copy-move detection benchmark datasets of color images. Results demonstrate the superiority of the proposed method over recently proposed state-of-the-art approaches in terms of well accepted performance metrics such as accuracy, area under ROC curve and others.
- Description: 2018 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2018
Enhanced colour image retrieval with cuboid segmentation
- Authors: Murshed, Manzur , Karmakar, Priyabrata , Teng, Shyh , Lu, Guojun
- Date: 2018
- Type: Text , Conference proceedings , Conference paper
- Relation: 2018 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2018; Canberra, Australia; 10th-13th December 2018
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- Description: In this paper, we further investigate our recently proposed cuboid image segmentation algorithm for effective image retrieval. Instead of using all cuboids (i.e. segments), we have proposed two approaches to choose different subsets of cuboids appropriately. With the experimental results on eBay dataset, we have shown that our proposals outperform retrieval performance of the existing technique. In addition, we have investigated how many segments are required for the most effective image retrieval and provide a quick method to determine the suitable number of cuboids.
- Description: 2018 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2018
Exploiting user provided information in dynamic consolidation of virtual machines to minimize energy consumption of cloud data centers
- Authors: Khan, Anit , Paplinski, Andrew , Khan, Abdul , Murshed, Manzur , Buyya, Rajkumar
- Date: 2018
- Type: Text , Conference proceedings , Conference paper
- Relation: 3rd International Conference on Fog and Mobile Edge Computing, FMEC 2018; Barcelona, Spain; 23rd-26th April 2018; p. 105-114
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- Description: Dynamic consolidation of Virtual Machines (VMs) can effectively enhance the resource utilization and energy-efficiency of the Cloud Data Centers (CDC). Existing research on Cloud resource reservation and scheduling signify that Cloud Service Users (CSUs) can play a crucial role in improving the resource utilization by providing valuable information to Cloud service providers. However, utilization of CSUs' provided information in minimization of energy consumption of CDC is a novel research direction. The challenges herein are twofold. First, finding the right benign information to be received from a CSU which can complement the energy-efficiency of CDC. Second, smart application of such information to significantly reduce the energy consumption of CDC. To address those research challenges, we have proposed a novel heuristic Dynamic VM Consolidation algorithm, RTDVMC, which minimizes the energy consumption of CDC through exploiting CSU provided information. Our research exemplifies the fact that if VMs are dynamically consolidated based on the time when a VM can be removed from CDC-a useful information to be received from respective CSU, then more physical machines can be turned into sleep state, yielding lower energy consumption. We have simulated the performance of RTDVMC with real Cloud workload traces originated from more than 800 PlanetLab VMs. The empirical figures affirm the superiority of RTDVMC over existing prominent Static and Adaptive Threshold based DVMC algorithms.
Passive detection of splicing and copy-move attacks in image forgery
- Authors: Islam, Mohammad , Kamruzzaman, Joarder , Karmakar, Gour , Murshed, Manzur , Kahandawa, Gayan
- Date: 2018
- Type: Text , Conference proceedings , Conference paper
- Relation: 25th International Conference on Neural Information Processing, ICONIP 2018; Siem Reap, Cambodia; 13th-16th December 2018; published in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 11304 LNCS, p. 555-567
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- Description: Internet of Things (IoT) image sensors for surveillance and monitoring, digital cameras, smart phones and social media generate huge volume of digital images every day. Image splicing and copy-move attacks are the most common types of image forgery that can be done very easily using modern photo editing software. Recently, digital forensics has drawn much attention to detect such tampering on images. In this paper, we introduce a novel feature extraction technique, namely Sum of Relevant Inter-Cell Values (SRIV) using which we propose a passive (blind) image forgery detection method based on Discrete Cosine Transformation (DCT) and Local Binary Pattern (LBP). First, the input image is divided into non-overlapping blocks and 2D block DCT is applied to capture the changes of a tampered image in the frequency domain. Then LBP operator is applied to enhance the local changes among the neighbouring DCT coefficients, magnifying the changes in high frequency components resulting from splicing and copy-move attacks. The resulting LBP image is again divided into non-overlapping blocks. Finally, SRIV is applied on the LBP image blocks to extract features which are then fed into a Support Vector Machine (SVM) classifier to identify forged images from authentic ones. Extensive experiment on four well-known benchmark datasets of tampered images reveal the superiority of our method over recent state-of-the-art methods.
Texture based vein biometrics for human identification : A comparative study
- Authors: Bashar, Khayrul , Murshed, Manzur
- Date: 2018
- Type: Text , Conference proceedings
- Relation: 42nd IEEE Computer Software and Applications Conference, COMPSAC 2018; Tokyo, Japan; 23rd-27th July 2018 Vol. 2, p. 571-576
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- Description: Hand vein biometric is an important modality for human authentication and liveness detection in many applications. Reliable feature extraction is vital to any biometric system. Over the past years, two major categories of vein features, namely vein structures and vein image textures, were proposed for hand dorsal vein based biometric identification. Of them, texture features seem important as it can combine skin micro-textures along with vein properties. In this study, we have performed a comparative study to identify potential texture features and feature-classifier combination that produce efficient vein biometric systems. Seven texture features (HOG, GABOR, GLCM, SSF, DWT, WPT, and LBP) and three multiclass classifiers (LDA, ESVM, and KNN) were explored towards the supervised identification of human from vein images. An experiment with 400 infrared (IR) hand images from 40 adults indicates the superior performance of the histogram of oriented gradients (HOG) and simple local statistical feature (SSF) with LDA and ESVM classifiers in terms of average accuracy (> 90%), average Fscore (> 58%) and average specificity (>93%). The decision-level fusion of the LDA and ESVM classifier with single texture features showed improved performances (by 2.2 to 13.2% of average Fscore) over individual classifier for human identification with IR hand vein images.
- Description: Proceedings - International Computer Software and Applications Conference
A novel quality metric using spatiotemporal correlational data of human eye maneuver
- Authors: Podder, Pallab , Paul, Manoranjan , Murshed, Manzur
- Date: 2017
- Type: Text , Conference proceedings
- Relation: 2017 International Conference on Digital Image Computing : Techniques and Applications, DICTA 2017; Sydney, Australia; 29th November-1st December 2017 Vol. 2017-December, p. 1-8
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- Reviewed:
- Description: The popularly used subjective estimator- mean opinion score (MOS) is often biased by the testing environment, viewers mode, domain expertise, and many other factors that may actively influence on actual assessment. We therefore, devise a no- reference subjective quality assessment metric by exploiting the nature of human eye browsing on videos. The participants' eye-tracker recorded gaze-data indicate more concentrated eye- traversing approach for relatively better quality. We calculate the Length, Angle, Pupil-size, and Gaze-duration features from the recorded gaze trajectory. The content and resolution invariant operation is carried out prior to synthesizing them using an adaptive weighted function to develop a new quality metric using eye traversal (QMET). Tested results reveal that the quality evaluation carried out by QMET demonstrates a strong correlation with the most widely used peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and the MOS.
- Description: DICTA 2017 - 2017 International Conference on Digital Image Computing: Techniques and Applications
Cuboid segmentation for effective image retrieval
- Authors: Murshed, Manzur , Teng, Shyh , Lu, Guojun
- Date: 2017
- Type: Text , Conference proceedings
- Relation: 2017 International Conference on Digital Image Computing : Techniques and Applications (DICTA); Sydney, Australia; 29th November-1st December 2017 p. 884-891
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- Description: Region-based image retrieval has been proven to be effective in finding relevant images. In this paper, we propose a cuboid im-age segmentation method which results in rectangle image partitions. Rectangle partitions are more suitable for image compression, retrieval and other image operations. We apply partitions in image retrieval in this paper. Our experimental results have shown that (1) the proposed partitioning method is effective in segmenting images into meaningful rectangles; (2) using colour partitions for image retrieval is more effective than using whole images; and (3) the partitioned approach has additional advantage of letting users to select certain objects/colours as queries to find more relevant images/objects. These three advantages could be important in crime scene investigation image indexing and retrieval. Moreover, the proposed technique is amenable to compressed-domain applications.
Lossless depth map coding using binary tree based decomposition and context-based arithmetic coding
- Authors: Shahriyar, Shampa , Murshed, Manzur , Ali, Mortuza , Paul, Manoranjan
- Date: 2016
- Type: Text , Conference proceedings , Conference paper
- Relation: 2016 IEEE International Conference on Multimedia and Expo, ICME 2016; Seattle, United States; 11th-15th July 2016; published in Proceedings of the 2016 IEEE International Conference on Mulitmedia and Expo Vol. 2016-August, p. 1-6
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- Description: Depth maps are becoming increasingly important in the context of emerging video coding and processing applications. Depth images represent the scene surface and are characterized by areas of smoothly varying grey levels separated by sharp edges at the position of object boundaries. To enable high quality view rendering at the receiver side, preservation of these characteristics is important. Lossless coding enables avoiding rendering artifacts in synthesized views due to depth compression artifacts. In this paper, we propose a binary tree based lossless depth coding scheme that arranges the residual frame into integer or binary residual bitmap. High spatial correlation in depth residual frame is exploited by creating large homogeneous blocks of adaptive size, which are then coded as a unit using context based arithmetic coding. On the standard 3D video sequences, the proposed lossless depth coding has achieved compression ratio in the range of 20 to 80. © 2016 IEEE.
- Description: Proceedings - IEEE International Conference on Multimedia and Expo
Lossless hyperspectral image compression using binary tree based decomposition
- Authors: Shahriyar, Shampa , Paul, Manoranjan , Murshed, Manzur , Ali, Mortuza
- Date: 2016
- Type: Text , Conference proceedings
- Relation: 2016 International Conference on Digital Image Computing: Techniques and Applications (Dicta); Gold Coast, Australia; 30th November-2nd December 2016 p. 428-435
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- Description: A Hyperspectral (HS) image provides observational powers beyond human vision capability but represents more than 100 times data compared to a traditional image. To transmit and store the huge volume of an HS image, we argue that a fundamental shift is required from the existing "original pixel intensity"based coding approaches using traditional image coders (e.g. JPEG) to the "residual" based approaches using a predictive coder exploiting band-wise correlation for better compression performance. Moreover, as HS images are used in detection or classification they need to be in original form; lossy schemes can trim off uninteresting data along with compression, which can be important to specific analysis purposes. A modified lossless HS coder is required to exploit spatial- spectral redundancy using predictive residual coding. Every spectral band of an HS image can be treated like they are the individual frame of a video to impose inter band prediction. In this paper, we propose a binary tree based lossless predictive HS coding scheme that arranges the residual frame into integer residual bitmap. High spatial correlation in HS residual frame is exploited by creating large homogeneous blocks of adaptive size, which are then coded as a unit using context based arithmetic coding. On the standard HS data set, the proposed lossless predictive coding has achieved compression ratio in the range of 1.92 to 7.94. In this paper, we compare the proposed method with mainstream lossless coders (JPEG-LS and lossless HEVC). For JPEG-LS, HEVCIntra and HEVCMain, proposed technique has reduced bit-rate by 35%, 40% and 6.79% respectively by exploiting spatial correlation in predicted HS residuals.
QMET : A new quality assessment metric for no-reference video coding by using human eye traversal
- Authors: Podder, Pallab , Paul, Manoranjan , Murshed, Manzur
- Date: 2016
- Type: Text , Conference proceedings
- Relation: 2016 International Conference on Image and Vision Computing New Zealand, IVCNZ 2016; Palmerston North, New Zealand; 21st-22nd November 2016 p. 1-6
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- Description: The subjective quality assessment (SQA) is an ever demanding approach due to its in-depth interactivity to the human cognition. The addition of no-reference based scheme could equip the SQA techniques to tackle further challenges. Existing widely used objective metrics-peak signal-to-noise ratio (PSNR), structural similarity index (SSIM) or the subjective estimator-mean opinion score (MOS) requires original image for quality evaluation that limits their uses for the situation having no-reference. In this work, we present a no-reference based SQA technique that could be an impressive substitute to the reference-based approaches for quality evaluation. The High Efficiency Video Coding (HEVC) reference test model (HM15.0) is first exploited to generate five different qualities of the HEVC recommended eight class sequences. To assess different aspects of coded video quality, a group of ten participants are employed and their eye-tracker (ET) recorded data demonstrate closer correlation among gaze plots for relatively better quality video contents. Therefore, we innovatively calculate the amount of approximation of smooth eye traversal (ASET) by using distance, angle, and pupil-size feature from recorded gaze trajectory data and develop a new-quality metric based on eye traversal (QMET). Experimental results show that the quality evaluation carried out by QMET is highly correlated to the HM recommended coding quality. The performance of the QMET is also compared with the PSNR and SSIM metrics to justify the effectiveness of each other.
- Description: International Conference Image and Vision Computing New Zealand
A novel depth motion vector coding exploiting spatial and inter-component clustering tendency
- Authors: Shahriyar, Shampa , Murshed, Manzur , Ali, Mortuza , Paul, Manoranjan
- Date: 2015
- Type: Text , Conference proceedings , Conference paper
- Relation: Visual Communications and Image Processing, VCIP 2015; Singapore; 13th-16th December 2015 p. 1-4
- Relation: http://purl.org/au-research/grants/arc/DP130103670
- Full Text: false
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- Description: Motion vectors of depth-maps in multiview and free-viewpoint videos exhibit strong spatial as well as inter-component clustering tendency. This paper presents a novel coding technique that first compresses the multidimensional bitmaps of macroblock mode and then encodes only the non-zero components of motion vectors. The bitmaps are partitioned into disjoint cuboids using binary tree based decomposition so that the 0's and 1's are either highly polarized or further sub-partitioning is unlikely to achieve any compression. Each cuboid is entropy-coded as a unit using binary arithmetic coding. This technique is capable of exploiting the spatial and inter-component correlations efficiently without the restriction of scanning the bitmap in any specific linear order as needed by run-length coding. As encoding of non-zero component values no longer requires denoting the zero value, further compression efficiency is achieved. Experimental results on standard multiview test video sequences have comprehensively demonstrated the superiority of the proposed technique, achieving overall coding gain against the state-of-the-art in the range [22%, 54%] and on average 38%. © 2015 IEEE.
- Description: 2015 Visual Communications and Image Processing, VCIP 2015
An analysis of human engagement behaviour using descriptors from human feedback, eye tracking, and saliency modelling
- Authors: Podder, Pallab , Paul, Manoranjan , Debnath, Tanmoy , Murshed, Manzur
- Date: 2015
- Type: Text , Conference proceedings
- Relation: 2015 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2015, Adelaide, 23-25th Nov 2015 in Digital Image Computing: Techniques and Applications (DICTA), 2015 International Conference
- Relation: http://purl.org/au-research/grants/arc/DP130103670
- Full Text: false
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- Description: In this paper an analysis of human engagement behaviour with video is presented based on real life experiments. An engagement model could be employed in classroom education, enhancing programming skills, reading etc. Two groups of people, independent of one another, watched eighteen video clips separately at different times. The first group's participants' eye gaze locations, right and left pupil sizes, and eye blinking patterns are recorded by a state of the art Tobii eye tracker. The second group of people who are video experts opined about the most significant attention points of the videos. A well-known bottom-up visual saliency model, Graph-Based Visual Saliency (GBVS), is also utilized to create salient points for the videos. Taking into consideration all the above mentioned descriptors the introduced behaviour analysis demonstrates the level of participants' concentration with the videos.
An efficient cooperative lane-changing algorithm for sensor- and communication-enabled automated vehicles
- Authors: Awal, Tanveer , Murshed, Manzur , Ali, Mortuza
- Date: 2015
- Type: Text , Conference proceedings
- Full Text: false
- Description: A key goal in transportation system is to attain efficient road traffic through minimization of trip time, fuel consumption and pollutant-emission without compromising safety. In dense traffic lane-changes and merging are often key ingredients to cause safety hazards, traffic breakdowns and travel delays. In this paper, we propose an efficient cooperative lane-changing algorithm CLA for sensor- and communication-enabled automated vehicles to reduce the lane-changing bottlenecks. For discretionary lane-changing, we consider the advantages of the subject vehicle, the follower in the current lane and k (an integer) lag vehicles in the target lane to maximize speed gains. Our algorithm simultaneously minimizes the impact of lane-change on traffic flow and the overall trip time, fuel-consumption and pollutant-emission. For mandatory lane-changing CLA dissociates the decision-making point from the actual mandatory lane-changing point and computes a suitable lane-changing slot in order to minimize lane-changing (merging) time. Our algorithm outperforms the potential cooperative lane-changing algorithm MOBIL proposed by Kesting et al. [1] in terms of merging time and rate, waiting time, fuel consumption, average velocity and flow (especially at the point in front of the merging point) at the cost of slightly increased average trip time for the mainroad vehicles compared to MOBIL. We also highlight important directions for further research. © 2015 IEEE.
Fast coding strategy for HEVC by motion features and saliency applied on difference between successive image blocks
- Authors: Podder, Pallab , Paul, Manoranjan , Murshed, Manzur
- Date: 2015
- Type: Text , Conference proceedings
- Relation: ConferencePacific-Rim Symposium on Image and Video Technology, Auckland, 23-27th Nov, 2016, In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics).9431 p. 175-186
- Relation: http://purl.org/au-research/grants/arc/DP130103670
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- Description: Introducing a number of innovative and powerful coding tools, the High Efficiency Video Coding (HEVC) standard promises double compression efficiency, compared to its predecessor H.264, with similar perceptual quality. The increased computational time complexity is an important issue for the video coding research community as well. An attempt to reduce this complexity of HEVC is adopted in this paper, by efficient selection of appropriate block-partitioning modes based on motion features and the saliency applied to the difference between successive image blocks. As this difference gives us the explicit visible motion and salient information, we develop a cost function by combining the motion features and image difference salient feature. The combined features are then converted into area of interest (AOI) based binary pattern for the current block. This pattern is then compared with a previously defined codebook of binary pattern templates for a subset of mode selection. Motion estimation (ME) and motion compensation (MC) are performed only on the selected subset of modes, without exhaustive exploration of all modes available in HEVC. The experimental results reveal a reduction of 42% encoding time complexity of HEVC encoder with similar subjective and objective image quality.
Fast intermode selection for HEVC video coding using phase correlation
- Authors: Podder, Pallab , Paul, Manoranjan , Murshed, Manzur , Chakraborty, Subrata
- Date: 2015
- Type: Text , Conference proceedings , Conference paper
- Relation: 2014 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2014; Wollongong, Australia; 25th-27th November 2014 p. 1-8
- Relation: http://purl.org/au-research/grants/arc/DP130103670
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- Description: The recent High Efficiency Video Coding (HEVC) Standard demonstrates higher rate-distortion (RD) performance compared to its predecessor H.264/AVC using different new tools especially larger and asymmetric inter-mode variable size motion estimation and compensation. This requires more than 4 times computational time compared to H.264/AVC. As a result it has always been a big concern for the researchers to reduce the amount of time while maintaining the standard quality of the video. The reduction of computational time by smart selection of the appropriate modes in HEVC is our motivation. To accomplish this task in this paper, we use phase correlation to approximate the motion information between current and reference blocks by comparing with a number of different binary pattern templates and then select a subset of motion estimation modes without exhaustively exploring all possible modes. The experimental results exhibit that the proposed HEVC-PC (HEVC with Phase Correlation) scheme outperforms the standard HEVC scheme in terms of computational time while preserving-the same quality of the video sequences. More specifically, around 40% encoding time is reduced compared to the exhaustive mode selection in HEVC. © 2014 IEEE.
- Description: 2014 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2014
Joint texture and depth coding using cuboid data compression
- Authors: Paul, Manoranjan , Chakraborty, Subrata , Murshed, Manzur , Podder, Pallab
- Date: 2015
- Type: Text , Conference proceedings
- Relation: 2015 18th International Conference on Computer and Information Technology (ICCIT); Dhaka, Bangladesh; 21st-23rd December 2015 p. 138-143
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- Description: The latest multiview video coding (MVC) standards such as 3D-HEVC and H.264/MVC normally encodes texture and depth videos separately. Significant amount of rate-distortion performance and computational performance are sacrificed due to separate encoding due to the lack of exploitation of joint information. Obviously, separate encoding also creates synchronization issue for 3D scene formation in the decoder. Moreover, the hierarchical frame referencing architecture in the MVC creates random access frame delay. In this paper we develop an encoder and decoder framework where we can encode texture and depth video jointly by forming and encoding 3D cuboid using high dimensional entropy coding. The results from our experiments show that our proposed framework outperforms the 3D-HEVC in rate-distortion performance and reduces the computational time significantly by reducing random access frame delay.