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Depth sequence coding with hierarchical partitioning and spatial-domain quantization

- Shahriyar, Shampa, Murshed, Manzur, Ali, Mortuza, Paul, Manoranjan

  • Authors: Shahriyar, Shampa , Murshed, Manzur , Ali, Mortuza , Paul, Manoranjan
  • Date: 2020
  • Type: Text , Journal article , Article
  • Relation: IEEE Transactions on Circuits and Systems for Video Technology Vol. 30, no. 3 (2020), p. 835-849
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  • Description: Depth coding in 3D-HEVC deforms object shapes due to block-level edge-approximation and lacks efficient techniques to exploit the statistical redundancy, due to the frame-level clustering tendency in depth data, for higher coding gain at near-lossless quality. This paper presents a standalone mono-view depth sequence coder, which preserves edges implicitly by limiting quantization to the spatial-domain and exploits the frame-level clustering tendency efficiently with a novel binary tree-based decomposition (BTBD) technique. The BTBD can exploit the statistical redundancy in frame-level syntax, motion components, and residuals efficiently with fewer block-level prediction/coding modes and simpler context modeling for context-adaptive arithmetic coding. Compared with the depth coder in 3D-HEVC, the proposed one has achieved significantly lower bitrate at lossless to near-lossless quality range for mono-view coding and rendered superior quality synthetic views from the depth maps, compressed at the same bitrate, and the corresponding texture frames. © 1991-2012 IEEE.

An efficient RANSAC hypothesis evaluation using sufficient statistics for RGB-D pose estimation

- Senthooran, Ilankalkone, Murshed, Manzur, Barca, Jan, Kamruzzaman, Joarder, Chung, Hoam

  • Authors: Senthooran, Ilankalkone , Murshed, Manzur , Barca, Jan , Kamruzzaman, Joarder , Chung, Hoam
  • Date: 2019
  • Type: Text , Journal article
  • Relation: Autonomous Robots Vol. 43, no. 5 (2019), p. 1257-1270
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  • Description: Achieving autonomous flight in GPS-denied environments begins with pose estimation in three-dimensional space, and this is much more challenging in an MAV in a swarm robotic system due to limited computational resources. In vision-based pose estimation, outlier detection is the most time-consuming step. This usually involves a RANSAC procedure using the reprojection-error method for hypothesis evaluation. Realignment-based hypothesis evaluation method is observed to be more accurate, but the considerably slower speed makes it unsuitable for robots with limited resources. We use sufficient statistics of least-squares minimisation to speed up this process. The additive nature of these sufficient statistics makes it possible to compute pose estimates in each evaluation by reusing previously computed statistics. Thus estimates need not be calculated from scratch each time. The proposed method is tested on standard RANSAC, Preemptive RANSAC and R-RANSAC using benchmark datasets. The results show that the use of sufficient statistics speeds up the outlier detection process with realignment hypothesis evaluation for all RANSAC variants, achieving an execution speed of up to 6.72 times.

Hierarchical colour image segmentation by leveraging RGB channels independently

- Tania, Sheikh, Murshed, Manzur, Teng, Shyh, Karmakar, Gour

  • Authors: Tania, Sheikh , Murshed, Manzur , Teng, Shyh , Karmakar, Gour
  • Date: 2019
  • Type: Text , Conference paper , Conference Paper
  • Relation: 9th Pacific-Rim Symposium on Image and Video Technology, PSIVT 2019 Vol. 11854 LNCS, p. 197-210
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  • Description: In this paper, we introduce a hierarchical colour image segmentation based on cuboid partitioning using simple statistical features of the pixel intensities in the RGB channels. Estimating the difference between any two colours is a challenging task. As most of the colour models are not perceptually uniform, investigation of an alternative strategy is highly demanding. To address this issue, for our proposed technique, we present a new concept for colour distance measure based on the inconsistency of pixel intensities of an image which is more compliant to human perception. Constructing a reliable set of superpixels from an image is fundamental for further merging. As cuboid partitioning is a superior candidate to produce superpixels, we use the agglomerative merging to yield the final segmentation results exploiting the outcome of our proposed cuboid partitioning. The proposed cuboid segmentation based algorithm significantly outperforms not only the quadtree-based segmentation but also existing state-of-the-art segmentation algorithms in terms of quality of segmentation for the benchmark datasets used in image segmentation. © 2019, Springer Nature Switzerland AG.

Measuring trustworthiness of IoT image sensor data using other sensors' complementary multimodal data

- Islam, Mohammad, Karmakar, Gour, Kamruzzaman, Joarder, Murshed, Manzur

  • 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
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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

- Podder, Pallab, Paul, Manoranjan, Murshed, Manzur

  • 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

- Tania, Sheikh, Murshed, Manzur, Teng, Shyh, Karmakar, Gour

Detecting splicing and copy-move attacks in color images

- Islam, Mohammad, Karmakar, Gour, Kamruzzaman, Joarder, Murshed, Manzur, Kahandawa, Gayan, Parvin, Nahida

  • Authors: Islam, Mohammad , Karmakar, Gour , Kamruzzaman, Joarder , Murshed, Manzur , Kahandawa, Gayan , Parvin, Nahida
  • 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 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

Efficient video coding using visual sensitive information for HEVC coding standard

- Podder, Pallab, Paul, Manoranjan, Murshed, Manzur

  • Authors: Podder, Pallab , Paul, Manoranjan , Murshed, Manzur
  • Date: 2018
  • Type: Text , Journal article
  • Relation: IEEE Access Vol. 6, no. (2018), p. 75695-75708
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  • Description: The latest high efficiency video coding (HEVC) standard introduces a large number of inter-mode block partitioning modes. The HEVC reference test model (HM) uses partially exhaustive tree-structured mode selection, which still explores a large number of prediction unit (PU) modes for a coding unit (CU). This impacts on encoding time rise which deprives a number of electronic devices having limited processing resources to use various features of HEVC. By analyzing the homogeneity, residual, and different statistical correlation among modes, many researchers speed-up the encoding process through the number of PU mode reduction. However, these approaches could not demonstrate the similar rate-distortion (RD) performance with the HM due to their dependency on existing Lagrangian cost function (LCF) within the HEVC framework. In this paper, to avoid the complete dependency on LCF in the initial phase, we exploit visual sensitive foreground motion and spatial salient metric (FMSSM) in a block. To capture its motion and saliency features, we use the dynamic background and visual saliency modeling, respectively. According to the FMSSM values, a subset of PU modes is then explored for encoding the CU. This preprocessing phase is independent from the existing LCF. As the proposed coding technique further reduces the number of PU modes using two simple criteria (i.e., motion and saliency), it outperforms the HM in terms of encoding time reduction. As it also encodes the uncovered and static background areas using the dynamic background frame as a substituted reference frame, it does not sacrifice quality. Tested results reveal that the proposed method achieves 32% average encoding time reduction of the HM without any quality loss for a wide range of videos.

Enhanced colour image retrieval with cuboid segmentation

- Murshed, Manzur, Karmakar, Priyabrata, Teng, Shyh, Lu, Guojun

  • 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

- Khan, Anit, Paplinski, Andrew, Khan, Abdul, Murshed, Manzur, Buyya, Rajkumar

  • 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

- Islam, Mohammad, Kamruzzaman, Joarder, Karmakar, Gour, Murshed, Manzur, Kahandawa, Gayan

  • 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

- Bashar, Khayrul, Murshed, Manzur

  • 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

- Podder, Pallab, Paul, Manoranjan, Murshed, Manzur

  • 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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  • 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

Adaptive weighted non-parametric background model for efficient video coding

- Chakraborty, Subrata, Paul, Manoranjan, Murshed, Manzur, Ali, Mortuza

  • Authors: Chakraborty, Subrata , Paul, Manoranjan , Murshed, Manzur , Ali, Mortuza
  • Date: 2017
  • Type: Text , Journal article
  • Relation: Neurocomputing Vol. 226, no. (2017), p. 35-45
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  • Description: Dynamic background frame based video coding using mixture of Gaussian (MoG) based background modelling has achieved better rate distortion performance compared to the H.264 standard. However, they suffer from high computation time, low coding efficiency for dynamic videos, and prior knowledge requirement of video content. In this paper, we introduce the application of the non-parametric (NP) background modelling approach for video coding domain. We present a novel background modelling technique, called weighted non-parametric (WNP) which balances the historical trend and the recent value of the pixel intensities adaptively based on the content and characteristics of any particular video. WNP is successfully embedded into the latest HEVC video coding standard for better rate-distortion performance. Moreover, a novel scene adaptive non-parametric (SANP) technique is also developed to handle video sequences with high dynamic background. Being non-parametric, the proposed techniques naturally exhibit superior performance in dynamic background modelling without a priori knowledge of video data distribution.

An algorithm for network and data-aware placement of multi-tier applications in cloud data centers

- Ferdaus, Md Hasanul, Murshed, Manzur, Calheiros, Rodrigo, Buyya, Rajkumar

  • Authors: Ferdaus, Md Hasanul , Murshed, Manzur , Calheiros, Rodrigo , Buyya, Rajkumar
  • Date: 2017
  • Type: Text , Journal article
  • Relation: Journal of Network and Computer Applications Vol. 98, no. (2017), p. 65-83
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  • Description: Today's Cloud applications are dominated by composite applications comprising multiple computing and data components with strong communication correlations among them. Although Cloud providers are deploying large number of computing and storage devices to address the ever increasing demand for computing and storage resources, network resource demands are emerging as one of the key areas of performance bottleneck. This paper addresses network-aware placement of virtual components (computing and data) of multi-tier applications in data centers and formally defines the placement as an optimization problem. The simultaneous placement of Virtual Machines and data blocks aims at reducing the network overhead of the data center network infrastructure. A greedy heuristic is proposed for the on-demand application components placement that localizes network traffic in the data center interconnect. Such optimization helps reducing communication overhead in upper layer network switches that will eventually reduce the overall traffic volume across the data center. This, in turn, will help reducing packet transmission delay, increasing network performance, and minimizing the energy consumption of network components. Experimental results demonstrate performance superiority of the proposed algorithm over other approaches where it outperforms the state-of-the-art network-aware application placement algorithm across all performance metrics by reducing the average network cost up to 67% and network usage at core switches up to 84%, as well as increasing the average number of application deployments up to 18%. © 2017 Elsevier Ltd

Cuboid segmentation for effective image retrieval

- Murshed, Manzur, Teng, Shyh, Lu, Guojun

  • 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.

Dynamic virtual machine consolidation algorithms for energy-efficient cloud resource management : A review

- Khan, Md Anit, Paplinski, Andrew, Khan, Abdul, Murshed, Manzur, Buyya, Rajkumar

  • Authors: Khan, Md Anit , Paplinski, Andrew , Khan, Abdul , Murshed, Manzur , Buyya, Rajkumar
  • Date: 2017
  • Type: Text , Book chapter
  • Relation: Sustainable Cloud and Energy Services : Principles and Practice Chapter 6 p. 135-165
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  • Description: As envisioned by Leonard Kleinrock [1], Cloud computing has transformed the dream of “computing as a utility” into reality, so much so it has turned out as the latest computing paradigm [2]. Cloud computing is called as Service-on-demand, as Cloud Service Providers (CSPs) assure users about potentially unlimited amount of resources that can be chartered on demand. It is also known as elastic computing, since Cloud Service Users (CSUs) can dynamically scale, expand, or shrink their rented resources anytime and expect to pay for the exact tenure of resource usage under Service Level Agreements (SLA). Through such flexibilities and financial benefits, CSPs have been attracting millions of clients who are simultaneously sharing the underlying computing and storage resources that are collectively known as Cloud data centers.

Improved depth coding for HEVC focusing on depth edge approximation

- Podder, Pallab, Paul, Manoranjan, Rahaman, Motiur, Murshed, Manzur

  • Authors: Podder, Pallab , Paul, Manoranjan , Rahaman, Motiur , Murshed, Manzur
  • Date: 2017
  • Type: Text , Journal article , acceptedVersion
  • Relation: Signal Processing: Image Communication Vol. 55, no. (2017), p. 80-92
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  • Description: The latest High Efficiency Video Coding (HEVC) standard has greatly improved the coding efficiency compared to its predecessor H.264. An important share of which is the adoption of hierarchical block partitioning structures and an extended number of modes. The structure of existing inter-modes is appropriate mainly to handle the rectangular and square aligned motion patterns. However, they could not be suitable for the block partitioning of depth objects having partial foreground motion with irregular edges and background. In such cases, the HEVC reference test model (HM) normally explores finer level block partitioning that requires more bits and encoding time to compensate large residuals. Since motion detection is the underlying criteria for mode selection, in this work, we use the energy concentration ratio feature of phase correlation to capture different types of motion in depth object. For better motion modeling focusing at depth edges, the proposed technique also uses an extra pattern mode comprising a group of templates with various rectangular and non-rectangular object shapes and edges. As the pattern mode could save bits by encoding only the foreground areas and beat all other inter-modes in a block once selected, the proposed technique could improve the rate-distortion performance. It could also reduce encoding time by skipping further branching using the pattern mode and selecting a subset of modes using innovative pre-processing criteria. Experimentally it could save 29% average encoding time and improve 0.10 dB Bjontegaard Delta peak signal-to-noise ratio compared to the HM.

A Centroid Algorithm for Stabilization of Turbulence-Degraded Underwater Videos

- Halder, Kalyan Kumar, Paul, Manoranjan, Tahtali, Murat, Anavatti, Sreenatha G., Murshed, Manzur

  • Authors: Halder, Kalyan Kumar , Paul, Manoranjan , Tahtali, Murat , Anavatti, Sreenatha G. , Murshed, Manzur
  • Date: 2016
  • Type: Text , Conference paper
  • Relation: 2016 International Conference on Digital Image Computing: Techniques and Applications DICTA 2016 p. 1-6
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  • Description: This paper addresses the problem of stabilizing underwater videos with non-uniform geometric deformations or warping due to a wavy water surface. It presents an improved method to correct these geometric deformations of the frames, providing a high-quality stabilized video output. For this purpose, a non-rigid image registration technique is employed to accurately align the warped frames with respect to a prototype frame and to estimate the deformation parameters, which in turn, are applied in an image dewarping technique. The prototype frame is chosen from the video sequence based on a sharpness assessment. The effectiveness of the proposed method is validated by applying it on both synthetic and real- world sequences using various quality metrics. A performance comparison with an existing method confirms the higher efficacy of the proposed method.

A novel depth edge prioritization based coding technique to boost-UP HEVC performance

- Podder, Pallab, Paul, Manoranjan, Murshed, Manzur

  • Authors: Podder, Pallab , Paul, Manoranjan , Murshed, Manzur
  • Date: 2016
  • Type: Text , Conference paper
  • Relation: 2016 IEEE International Conference on Multimedia & Expo Workshops (ICMEW)
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  • Description: In addition to the texture, multiview video employs the utilization of depth coding for the reconstruction of 3D video and Free viewpoint video. Standing on some texture-depth correlations, a number of methods in literature reuses texture motion vector for the corresponding depth coding to reduce encoding time by avoiding costly motion estimation process. However, texture similarity metric is not always equivalent to the corresponding depth similarity metric especially at edge levels. Since their approaches could not explicitly detect and encode acute edge motions of depth objects, eventually, could not reach the similar or improved rate-distortion (RD) performance against the High Efficiency Video Coding (HEVC) reference test model (HM). With a view to more accurate motion detection and modeling, the proposed technique exploits an extra Pattern Mode comprising a group of pattern templates (GPTs) with different rectangular and non-rectangular object shapes and edges compared to the existing HEVC block partitioning modes. Moreover, the proposed Pattern Mode only encodes the motion areas and skips the background areas. The experimental results show that the proposed technique could save 30% encoding time and improve average 0.1dB Bjontegard Delta peak signal-to-noise ratio (BD-PSNR) compared to the HM.