Discrete cosine basis oriented motion modeling with cuboidal applicability regions for versatile video coding
- Authors: Ahmmed, Ashek , Hamidouche, Wassim , Lambert, Andrew , Pickering, Mark , Murshed, Manzur
- Date: 2022
- Type: Text , Conference paper
- Relation: 2022 Picture Coding Symposium, PCS 2022, San Jose, Costa Rica, 7-9 December 2022, 2022 Picture Coding Symposium, PCS 2022 - Proceedings p. 337-341
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- Description: The relentless expansion of video based applications is underpinned by video coding technologies. The latest video coding standard i.e. versatile video coding (VVC) can provide superior compression performance than its predecessors. In this regard, motion modeling plays a central role. Experimental results showed that the discrete cosine basis oriented motion model can describe complex motion better than an affine motion model, adopted in the VVC. Hence, in this paper we propose to augment the VVC motion modeling technique with a set of discrete cosine basis oriented motion models and the applicability region of each such motion model is determined by non-overlapping rectangular regions, known as cuboids. Experimental results show a bit rate savings of up to 2.37% is achievable with respect to a VVC reference. © 2022 IEEE.
Dynamic mesh commonality modeling using the cuboidal partitioning
- Authors: Ahmmed, Ashek , Paul, Manoranjan , Murshed, Manzur , Pickering, Mark
- Date: 2022
- Type: Text , Conference paper
- Relation: 2022 IEEE International Conference on Visual Communications and Image Processing, VCIP 2022, Suzhou, China, 13-16 December 2022, 2022 IEEE International Conference on Visual Communications and Image Processing, VCIP 2022
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- Description: For 3D object representation, volumetric contents like meshes and point clouds provide suitable formats. However, a dynamic mesh sequence may require significantly large amount of data because it consists of information that varies with time. Hence, for the facilitation of storage and transmission of such content, efficient compression technologies are required. MPEG has started standardization activities aiming to develop a mesh compression standard that would be able to handle dynamic meshes with time varying connectivity information and time varying attribute maps. The attribute maps are features associated with the mesh surface and stored as 2D images/videos. In this paper, we propose to capture the commonality information in the dynamic mesh attribute maps using the cuboidal partitioning algorithm. This algorithm is capable of modeling both the global and local commonality within an image in a compact and computationally efficient way. Experimental results show that the proposed approach can outperform the anchor HEVC codec, suggested by MPEG to encode such sequences, with a bit rate savings of up to 3.66%. © 2022 IEEE.
Efficient scalable 360-degree video compression scheme using 3d cuboid partitioning
- Authors: Afsana, Fariha , Paul, Manoranjan , Murshed, Manzur , Taubman, David
- Date: 2022
- Type: Text , Conference paper
- Relation: 29th IEEE International Conference on Image Processing, ICIP 2022 p. 996-1000
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- Description: Video coding techniques minimize spatial and temporal redundancies inherent in video sequences based on non-overlapping block-based image partitioning. Due to depending on the information from already encoded neighboring blocks, these algorithms lack efficient techniques to exploit the overall global redundancies. Compared to the traditional block-based coding, the cuboid coding (2D) framework has been proven to be a more effective method of image compression that exploits global redundancy by considering homogeneous pixel correlation within a frame. In this paper, we improved the idea of 2D cuboid coding to exploit both local and global redundancy from a video sequence by adopting a three-dimensional (3D) cuboid partitioning scheme for SHVC compression improvement of 360-degree videos. The proposed method considers a group of successive frames as a 3D cuboid and recursively partitions it into sub-3D cuboids where static information over a selected GOP share the same cuboid and moving regions share new cuboids with better-defined objects. All the 3D cuboids are then encoded to create a coarse representation of the video stream. Experiments indicate that the proposed framework significantly outperforms its relevant benchmarks, notably by 17.18% (average) in BD-Rate reduction and 0.82 dB in BD-PSNR gain with respect to the standard SHVC codec. © 2022 IEEE.
Human pose based video compression via forward-referencing using deep learning
- Authors: Rajin, S.M. Ataul Karim , Murshed, Manzur , Paul, Manoranjan , Teng, Shyh , Ma, Jiangang
- Date: 2022
- Type: Text , Conference paper
- Relation: 2022 IEEE International Conference on Visual Communications and Image Processing, VCIP 2022, Suzhou, China,13-16 December 2022, 2022 IEEE International Conference on Visual Communications and Image Processing, VCIP 2022
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- Description: To exploit high temporal correlations in video frames of the same scene, the current frame is predicted from the already-encoded reference frames using block-based motion estimation and compensation techniques. While this approach can efficiently exploit the translation motion of the moving objects, it is susceptible to other types of affine motion and object occlusion/deocclusion. Recently, deep learning has been used to model the high-level structure of human pose in specific actions from short videos and then generate virtual frames in future time by predicting the pose using a generative adversarial network (GAN). Therefore, modelling the high-level structure of human pose is able to exploit semantic correlation by predicting human actions and determining its trajectory. Video surveillance applications will benefit as stored 'big' surveillance data can be compressed by estimating human pose trajectories and generating future frames through semantic correlation. This paper explores a new way of video coding by modelling human pose from the already-encoded frames and using the generated frame at the current time as an additional forward-referencing frame. It is expected that the proposed approach can overcome the limitations of the traditional backward-referencing frames by predicting the blocks containing the moving objects with lower residuals. Our experimental results show that the proposed approach can achieve on average up to 2.83 dB PSNR gain and 25.93% bitrate savings for high motion video sequences compared to standard video coding. © 2022 IEEE.
Detection of Malleefowl Mounds from Point Cloud Data
- Authors: Parvin, Nahida , Awrangjeb, Mohammad , Irvin, Marc , Florentine, Singarayer , Murshed, Manzur , Lu, Guojun
- Date: 2021
- Type: Text , Conference paper
- Relation: 2021 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2021, Gold Coast, 29 November to 1 December 2021
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- Description: Airborne light detection and ranging (LiDAR) data have become cost and time-efficient means for estimating the size of timid fauna populations through the identification of artefacts that evidence their occurrence in a large, hostile geographic area. The unobtrusive detection method helps conservation managers to assess the stability of a population and to design appropriate conservation programs. Here we propose a mound (nest) detection method for Australia's native iconic bird, the Malleefowl, from point cloud data, which can be manipulated to act as a surrogate for population data. Existing detection methods are largely through manual observations, and are therefore not efficient for covering large and remote areas. The proposed mound detection method can identify mound feature based on height and intensity values provided by the point cloud data. Each candidate mound point is initially selected by applying a height threshold utilising the classified ground points and their corresponding digital elevation model (DEM). Then, another threshold based on intensity range derived from ground truth mound area analysis is applied on the extracted initial mound points to find the final candidate mound points. These extracted points are then used to generate a binary mask where the potential mound points are found sparse. To connect those points, a morphological filter is applied on the binary image and found the mound separated from other remaining non-mound objects. To obtain the mound from other non-mound objects, a morphological cleaning operation and a connected component analysis are carried out on the mask. The non-mound objects are removed from the mask utilising the area property of mound derived from the empirical analysis of ground-truth observations. Finally, the effectiveness of the proposed technique is calculated based on ground truth. Although the mound shapes and structures are highly variable in nature, our height and intensity-based mound point extraction method detected 55 % of the ground-truthed mounds. © 2021 IEEE.
Dynamic point cloud compression using a cuboid oriented discrete cosine based motion model
- Authors: Ahmmed, Ashek , Paul, Manoranjan , Murshed, Manzur , Taubman, David
- Date: 2021
- Type: Text , Conference paper
- Relation: 2021 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2021 Vol. 2021-June, p. 1935-1939
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- Description: Immersive media representation format based on point clouds has underpinned significant opportunities for extended reality applications. Point cloud in its uncompressed format require very high data rate for storage and transmission. The video based point cloud compression technique projects a dynamic point cloud into geometry and texture video sequences. The projected texture video is then coded using modern video coding standard like HEVC. Since the properties of projected texture video frames are different from traditional video frames, HEVC-based commonality modeling can be inefficient. An improved commonality modeling technique is proposed that employs discrete cosine basis oriented motion models and the domains of such models are approximated by homogeneous regions called cuboids. Experimental results show that the proposed commonality modeling technique can yield savings in bit rate of up to 4.17%. ©2021 IEEE
Dynamic point cloud geometry compression using cuboid based commonality modelling framework
- Authors: Ahmmed, Ashek , Paul, Manoranjan , Murshed, Manzur , Taubman, David
- Date: 2021
- Type: Text , Conference paper
- Relation: 2021 IEEE International Conference on Image Processing, ICIP 2021, Anchorage, USA, 19-21 September 2021, Proceedings - International Conference on Image Processing, ICIP Vol. 2021-September, p. 2159-2163
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- Description: Point cloud in its uncompressed format require very high data rate for storage and transmission. The video based point cloud compression (V-PCC) technique projects a dynamic point cloud into geometry and texture video sequences. The projected geometry and texture video frames are then encoded using modern video coding standard like HEVC. However, HEVC encoder is unable to exploit the global commonality that exists within a geometry frame and between successive geometry frames to a greater extent. This is because in HEVC, the current frame partitioning starts from a rigid 64 × 64 pixels level without considering the structure of the scene need be coded. In this paper, an improved commonality modeling framework is proposed, by leveraging on cuboid-based frame partitioning, to encode point cloud geometry frames. The associated frame-partitioning scheme is based on statistical properties of the current geometry frame and therefore yields a flexible block partitioning structure composed of cuboids. Additionally, the proposed commonality modeling approach is computationally efficient and has a compact representation. Experimental results show that if the V-PCC reference encoder is augmented by the proposed commonality modeling technique, a bit rate savings of 2.71% and 4.25% are achieved for full body and upper body of human point clouds’ geometry sequences respectively. © 2021 IEEE.
Human-machine collaborative video coding through cuboidal partitioning
- Authors: Ahmmed, Ashek , Paul, Manoranjan , Murshed, Manzur , Taubman, David
- Date: 2021
- Type: Text , Conference paper
- Relation: 2021 IEEE International Conference on Image Processing, ICIP 2021, Anchorage, USA 19-22 September 2021, Proceedings - International Conference on Image Processing, ICIP Vol. 2021-September, p. 2074-2078
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- Description: Video coding algorithms encode and decode an entire video frame while feature coding techniques only preserve and communicate the most critical information needed for a given application. This is because video coding targets human perception, while feature coding aims for machine vision tasks. Recently, attempts are being made to bridge the gap between these two domains. In this work, we propose a video coding framework by leveraging on to the commonality that exists between human vision and machine vision applications using cuboids. This is because cuboids, estimated rectangular regions over a video frame, are computationally efficient, has a compact representation and object centric. Such properties are already shown to add value to traditional video coding systems. Herein cuboidal feature descriptors are extracted from the current frame and then employed for accomplishing a machine vision task in the form of object detection. Experimental results show that a trained classifier yields superior average precision when equipped with cuboidal features oriented representation of the current test frame. Additionally, this representation costs 7% less in bit rate if the captured frames are need be communicated to a receiver. © 2021 IEEE.
A coarse representation of frames oriented video coding by leveraging cuboidal partitioning of image data
- Authors: Ahmmed, Ashe , Paul, Manoranjan , Murshed, Manzur , Taubman, David
- Date: 2020
- Type: Text , Conference paper
- Relation: 22nd IEEE International Workshop on Multimedia Signal Processing, MMSP 2020, Virtual Tampere, Finland 21-24 September 2020
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- Description: Video coding algorithms attempt to minimize the significant commonality that exists within a video sequence. Each new video coding standard contains tools that can perform this task more efficiently compared to its predecessors. In this work, we form a coarse representation of the current frame by minimizing commonality within that frame while preserving important structural properties of the frame. The building blocks of this coarse representation are rectangular regions called cuboids, which are computationally simple and has a compact description. Then we propose to employ the coarse frame as an additional source for predictive coding of the current frame. Experimental results show an improvement in bit rate savings over a reference codec for HEVC, with minor increase in the codec computational complexity. © 2020 IEEE.
An Enhanced Local Texture Descriptor for Image Segmentation
- Authors: Tania, Sheikh , Murshed, Manzur , Teng, Shyh , Karmakar, Gour
- Date: 2020
- Type: Text , Conference paper
- Relation: 2020 IEEE International Conference on Image Processing, ICIP 2020 Vol. 2020-October, p. 1526-1530
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- Description: Texture is an indispensable property to develop many vision based autonomous applications. Compared to colour, feature dimension in a local texture descriptor is quite large as dense texture features need to represent the distribution of pixel intensities in the neighbourhood of each pixel. Large dimensional features require additional time for further processing that often restrict real-time applications. In this paper, a robust local texture descriptor is enhanced by reducing feature dimension by three folds without compromising the accuracy in region-based image segmentation applications. Reduction in feature dimension is achieved by exploiting the mean of neighbourhood pixel intensities radially along lines across a certain radius, which eliminates the need for sampling intensity distribution at three scales. Both the results of benchmark metrics and computational time are promising when the enhanced texture feature is used in a region-based hierarchical segmentation algorithm, a recent state-of-the-art technique. © 2020 IEEE.
Efficient low bit-rate intra-frame coding using common information for 360-degree video
- Authors: Afsana, Fariha , Paul, Manoranjan , Murshed, Manzur , Taubman, David
- Date: 2020
- Type: Text , Conference paper
- Relation: 22nd IEEE International Workshop on Multimedia Signal Processing, MMSP 2020
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- Description: With the growth of video technologies, super-resolution videos, including 360-degree immersive video has become a reality due to exciting applications such as augmented/virtual/mixed reality for better interaction and a wide-angle user-view experience of a scene compared to traditional video with narrow-focused viewing angle. The new generation video contents are bandwidth-intensive in nature due to high resolution and demand high bit rate as well as low latency delivery requirements that pose challenges in solving the bottleneck of transmission and storage burdens. There is limited optimisation space in traditional video coding schemes for improving video coding efficiency in intra-frame due to the fixed size of processing block. This paper presents a new approach for improving intra-frame coding especially at low bit rate video transmission for 360-degree video for lossy mode of HEVC. Prior to using traditional HEVC intra-prediction, this approach exploits the global redundancy of entire frame by extracting common important information using multi-level discrete wavelet transformation. This paper demonstrates that the proposed method considering only low frequency information of a frame and encoding this can outperform the HEVC standard at low bit rates. The experimental results indicate that the proposed intra-frame coding strategy achieves an average of 54.07% BD-rate reduction and 2.84 dB BD-PSNR gain for low bit rate scenario compared to the HEVC. It also achieves a significant improvement in encoding time reduction of about 66.84% on an average. Moreover, this finding also demonstrates that the existing HEVC block partitioning can be applied in the transform domain for better exploitation of information concentration as we applied HEVC on wavelet frequency domain. © 2020 IEEE.
Hierarchical colour image segmentation by leveraging RGB channels independently
- Authors: Tania, Sheikh , Murshed, Manzur , Teng, Shyh , Karmakar, Gour
- Date: 2019
- Type: Text , 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.
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
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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
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.
A Centroid Algorithm for Stabilization of Turbulence-Degraded Underwater Videos
- 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
- 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.
Anonymization techniques for preserving data quality in participatory sensing
- Authors: Sabrina, Tishna , Murshed, Manzur , Iqbal, Anindya
- Date: 2016
- Type: Text , Conference paper
- Relation: 2016 IEEE 41st Conference on Local Computer Networks (LCN) p. 607-610
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- Description: Participatory sensing is a revolutionary new paradigm where citizens voluntarily sense their surroundings using readily available sensing devices such as mobile phones and share this information for mutual benefit of community members. To encourage ample participation of users, ensuring their privacy is inevitable. Existing techniques that attempt to protect location privacy with spatial cloaking suffer from irrecoverable data quality degradation. To the best of our knowledge, very few works provided a solution preserving high data quality/utility at the destination server, however, suffered from unacceptable computational overhead. This paper presents an improved deterministic alternative and also a faster variant by exploiting several optimization issues. Theoretical formulations and extensive simulation results are presented to establish the applicability of our proposed techniques.