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Liu, D. & Flierl, M. (2019). Fractional-Pel Accurate Motion-Adaptive Transforms. IEEE Transactions on Image Processing, 28(6), 2731-2742, Article ID 8590746.
Open this publication in new window or tab >>Fractional-Pel Accurate Motion-Adaptive Transforms
2019 (English)In: IEEE Transactions on Image Processing, ISSN 1057-7149, E-ISSN 1941-0042, Vol. 28, no 6, p. 2731-2742, article id 8590746Article in journal (Refereed) Published
Abstract [en]

Fractional-pel accurate motion is widely used in video coding. For subband coding, fractional-pel accuracy is challenging since it is difficult to handle the complex motion field with temporal transforms. In our previous work, we designed integer accurate motion-adaptive transforms (MAT) which can transform integer accurate motion-connected coefficients. In this paper, we extend the integer MAT to fractional-pel accuracy. The integer MAT allows only one reference coefficient to be the lowhand coefficient. In this paper, we design the transform such that it permits multiple references and generates multiple low-band coefficients. In addition, our fractional-pel MAT can incorporate a general interpolation filter into the basis vector, such that the highband coefficient produced by the transform is the same as the prediction error from the interpolation filter. The fractional-pel MAT is always orthonormal. Thus, the energy is preserved by the transform. We compare the proposed fractional-pel MAT, the integer MAT, and the half-pel motion-compensated orthogonal transform (MCOT), while HEVC intra coding is used to encode the temporal subbands. The experimental results show that the proposed fractional-pel MAT outperforms the integer MAT and the half-pel MCOT. The gain achieved by the proposed MAT over the integer MAT can reach up to 1 dB in PSNR.

Place, publisher, year, edition, pages
IEEE, 2019
Keywords
Fractional-pel accurate motion; motionadaptive transforms; orthonormal transforms for video coding
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:kth:diva-248684 (URN)10.1109/TIP.2018.2889917 (DOI)000462386000008 ()30596576 (PubMedID)2-s2.0-85059251036 (Scopus ID)
Funder
Swedish Research Council, 2011-5841
Note

QC 20190424

Available from: 2019-04-09 Created: 2019-04-09 Last updated: 2019-04-24Bibliographically approved
Wu, H. & Flierl, M. (2018). Component-based quadratic similarity identification for multivariate Gaussian sources. In: Data Compression Conference Proceedings: . Paper presented at 2018 Data Compression Conference, DCC 2018, 27 March 2018 through 30 March 2018. Institute of Electrical and Electronics Engineers Inc.
Open this publication in new window or tab >>Component-based quadratic similarity identification for multivariate Gaussian sources
2018 (English)In: Data Compression Conference Proceedings, Institute of Electrical and Electronics Engineers Inc. , 2018Conference paper, Published paper (Refereed)
Abstract [en]

This paper considers the problem of compression for similarity identification. Unlike classic compression problems, the focus is not on reconstructing the original data. Instead, compression is determined by the reliability of answering given queries. The problem is characterized by the identification rate of a source which is the minimum compression rate which allows reliable answers for a given similarity threshold. In this work, we investigate the component-based quadratic similarity identification for multivariate Gaussian sources. The decorrelated original data is processed by a distinct D- A dmissible system for each component. For a special case, we characterize the component-based identification rate for a correlated Gaussian source. Furthermore, we derived the optimal bit allocation for a given total rate constraint.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers Inc., 2018
Keywords
Bit allocation, Similarity identification, Gaussian distribution, Component based, Compression rates, Gaussian sources, Identification rates, Optimal bit allocation, Rate constraints, Similarity threshold, Data compression
National Category
Computer and Information Sciences
Identifiers
urn:nbn:se:kth:diva-238074 (URN)10.1109/DCC.2018.00086 (DOI)2-s2.0-85050969530 (Scopus ID)9781538648834 (ISBN)
Conference
2018 Data Compression Conference, DCC 2018, 27 March 2018 through 30 March 2018
Note

Conference code: 138136; Export Date: 30 October 2018; Conference Paper; CODEN: DDCCF

QC 20180114

Available from: 2019-01-14 Created: 2019-01-14 Last updated: 2019-01-14Bibliographically approved
Wu, H., Wang, Q. & Flierl, M. (2018). Identification Rates for Block-correlated Gaussian Sources. In: Matthews, M B (Ed.), 2018 CONFERENCE RECORD OF 52ND ASILOMAR CONFERENCE ON SIGNALS, SYSTEMS, AND COMPUTERS: . Paper presented at 52nd Asilomar Conference on Signals, Systems, and Computers, OCT 28-NOV 01, 2018, Pacific Grove, CA (pp. 2114-2118). IEEE
Open this publication in new window or tab >>Identification Rates for Block-correlated Gaussian Sources
2018 (English)In: 2018 CONFERENCE RECORD OF 52ND ASILOMAR CONFERENCE ON SIGNALS, SYSTEMS, AND COMPUTERS / [ed] Matthews, M B, IEEE , 2018, p. 2114-2118Conference paper, Published paper (Refereed)
Abstract [en]

Among many current data processing systems, the objectives are often not the reproduction of data, but to compute some answers based on the data responding to sonic queries. The similarity identification task is to identify the items in a database which are similar to a given query item regarding to a certain metric. The problem of compression for similarity identification has been studied in [1]. Unlike classic compression problems, the focus is not on reconstructing the original data. Instead, the compression rate is determined by the desired reliability of the answers. Specifically, the information measure identification rate of a compression scheme characterizes the minimum compression rate that can be achieved which guarantees reliable answers with respect to a given similarity threshold. In this paper, we study the component-based quadratic similarity identification for correlated sources. The blocks are first decorrelated by Karhunen-Loeve transform. Then, the decorrelated data is processed by a distinct D-admissible system for each component. We derive the identification rate of component-based scheme for block correlated Gaussian sources. In addition, we characterize the identification rate of a special setting where any information regarding to the component similarity thresholds is unknown while only the similarity threshold of the whole scheme is given. Furthermore, we prove that block-correlated Gaussian sources are the "most difficult" to code under the special setting.

Place, publisher, year, edition, pages
IEEE, 2018
Series
Conference Record of the Asilomar Conference on Signals Systems and Computers, ISSN 1058-6393
National Category
Computer and Information Sciences
Identifiers
urn:nbn:se:kth:diva-252677 (URN)10.1109/ACSSC.2018.8645306 (DOI)000467845100373 ()2-s2.0-85062960125 (Scopus ID)978-1-5386-9218-9 (ISBN)
Conference
52nd Asilomar Conference on Signals, Systems, and Computers, OCT 28-NOV 01, 2018, Pacific Grove, CA
Note

QC 20190603

Available from: 2019-06-03 Created: 2019-06-03 Last updated: 2019-07-31Bibliographically approved
Wu, H., Wang, Q. & Flierl, M. (2018). PREDICTION-BASED SIMILARITY IDENTIFICATION FOR AUTOREGRESSIVE PROCESSES. In: 2018 IEEE GLOBAL CONFERENCE ON SIGNAL AND INFORMATION PROCESSING (GLOBALSIP 2018): . Paper presented at IEEE Global Conference on Signal and Information Processing (GlobalSIP), NOV 26-29, 2018, Anaheim, CA (pp. 266-270). IEEE
Open this publication in new window or tab >>PREDICTION-BASED SIMILARITY IDENTIFICATION FOR AUTOREGRESSIVE PROCESSES
2018 (English)In: 2018 IEEE GLOBAL CONFERENCE ON SIGNAL AND INFORMATION PROCESSING (GLOBALSIP 2018), IEEE , 2018, p. 266-270Conference paper, Published paper (Refereed)
Abstract [en]

The task of similarity identification is to identify items in a database which are similar to a given query item for a given metric. The identification rate of a compression scheme characterizes the minimum rate that can be achieved which guarantees reliable answers with respect to a given similarity threshold [1]. In this paper, we study a prediction-based quadratic similarity identification for autoregressive processes. We use an ideal linear predictor to remove linear dependencies in autoregressive processes. The similarity identification is conducted on the residuals. We show that the relation between the distortion of query and database processes and the distortion of their residuals is characterized by a sequence of eigenvalues. We derive the identification rate of our prediction-based approach for autoregressive Gaussian processes. We characterize the identification rate for the special case where only the smallest value in the sequence of eigenvalues is required to be known and derive its analytical upper bound by approximating a sequence of matrices with a sequence of Toeplitz matrices.

Place, publisher, year, edition, pages
IEEE, 2018
Series
IEEE Global Conference on Signal and Information Processing, ISSN 2376-4066
National Category
Computer and Information Sciences
Identifiers
urn:nbn:se:kth:diva-249832 (URN)10.1109/GlobalSIP.2018.8646407 (DOI)000462968100054 ()2-s2.0-85063103300 (Scopus ID)978-1-7281-1295-4 (ISBN)
Conference
IEEE Global Conference on Signal and Information Processing (GlobalSIP), NOV 26-29, 2018, Anaheim, CA
Note

QC 20190423

Available from: 2019-04-23 Created: 2019-04-23 Last updated: 2019-04-23Bibliographically approved
Wu, H. & Flierl, M. (2018). Transform-based compression for quadratic similarity queries. In: Conference Record of 51st Asilomar Conference on Signals, Systems and Computers, ACSSC 2017: . Paper presented at 51st Asilomar Conference on Signals, Systems and Computers, ACSSC 2017, Asilomar Hotel and Conference Grounds, Pacific Grove, United States, 29 October 2017 through 1 November 2017 (pp. 377-381). Institute of Electrical and Electronics Engineers (IEEE)
Open this publication in new window or tab >>Transform-based compression for quadratic similarity queries
2018 (English)In: Conference Record of 51st Asilomar Conference on Signals, Systems and Computers, ACSSC 2017, Institute of Electrical and Electronics Engineers (IEEE), 2018, p. 377-381Conference paper, Published paper (Refereed)
Abstract [en]

This paper considers the problem of compression for similarity queries [1] and discusses transform-based compression schemes. Here, the focus is on the tradeoff between the rate of the compressed data and the reliability of the answers to a given query. We consider compression schemes that do not allow false negatives when answering queries. Hence, classical compression techniques need to be modified. We propose transform-based compression schemes which decorrelate original data and regard each transform component as a distinct D-admissible system. Both compression and retrieval will be performed in the transform domain. The transform-based schemes show advantages in terms of encoding speed and the ability of handling high-dimensional correlated data. In particular, we discuss component-based and vector-based schemes. We use P{maybe}, a probability that is related to the occurrence of false positives to assess our scheme. Our experiments show that component-based schemes offer both good performance and low search complexity.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2018
Series
Conference Record of the Asilomar Conference on Signals Systems and Computers, ISSN 1058-6393
National Category
Telecommunications
Identifiers
urn:nbn:se:kth:diva-233721 (URN)10.1109/ACSSC.2017.8335363 (DOI)000442659900065 ()2-s2.0-85050980426 (Scopus ID)9781538618233 (ISBN)
Conference
51st Asilomar Conference on Signals, Systems and Computers, ACSSC 2017, Asilomar Hotel and Conference Grounds, Pacific Grove, United States, 29 October 2017 through 1 November 2017
Note

QC 20180830

Available from: 2018-08-30 Created: 2018-08-30 Last updated: 2018-09-17Bibliographically approved
Al-Zubaidy, H., Fodor, V., Dán, G. & Flierl, M. (2017). Reliable Video Streaming With Strict Playout Deadline in Multihop Wireless Networks. IEEE transactions on multimedia, 19(10), 2238-2251
Open this publication in new window or tab >>Reliable Video Streaming With Strict Playout Deadline in Multihop Wireless Networks
2017 (English)In: IEEE transactions on multimedia, ISSN 1520-9210, E-ISSN 1941-0077, Vol. 19, no 10, p. 2238-2251Article in journal (Refereed) Published
Abstract [en]

Motivated by emerging vision-based intelligent services, we consider the problem of rate adaptation for high-quality and low-delay visual information delivery over wireless networks using scalable video coding. Rate adaptation in this setting is inherently challenging due to the interplay between the variability of the wireless channels, the queuing at the network nodes, and the frame-based decoding and playback of the video content at the receiver at very short time scales. To address the problem, we propose a low-complexity model-based rate adaptation algorithm for scalable video streaming systems, building on a novel performance model based on stochastic network calculus. We validate the analytic model using extensive simulations. We show that it allows fast near-optimal rate adaptation for fixed transmission paths, as well as cross-layer optimized routing and video rate adaptation in mesh networks, with less than 10% quality degradation compared to the best achievable performance.

Place, publisher, year, edition, pages
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2017
Keywords
Multihop fading channels, network calculus, performance analysis, scalable video coding, wireless multimedia
National Category
Telecommunications Software Engineering
Identifiers
urn:nbn:se:kth:diva-215437 (URN)10.1109/TMM.2017.2742399 (DOI)000411247600009 ()2-s2.0-85028475488 (Scopus ID)
Note

QC 20171019

Available from: 2017-10-19 Created: 2017-10-19 Last updated: 2018-01-13Bibliographically approved
Wu, H., Wang, Q. & Flierl, M. (2017). Tree-Structured Vector Quantization for Similarity Queries. In: Bilgin, A Marcellin, MW SerraSagrista, J Storer, JA (Ed.), 2017 Data Compression Conference (DCC): . Paper presented at Data Compression Conference (DCC), APR 04-07, 2017, Snowbird, UT (pp. 467-467). IEEE Computer Society
Open this publication in new window or tab >>Tree-Structured Vector Quantization for Similarity Queries
2017 (English)In: 2017 Data Compression Conference (DCC) / [ed] Bilgin, A Marcellin, MW SerraSagrista, J Storer, JA, IEEE Computer Society, 2017, p. 467-467Conference paper, Published paper (Refereed)
Place, publisher, year, edition, pages
IEEE Computer Society, 2017
Series
IEEE Data Compression Conference, ISSN 1068-0314
National Category
Communication Systems
Identifiers
urn:nbn:se:kth:diva-243540 (URN)10.1109/DCC.2017.72 (DOI)000404240300077 ()978-1-5090-6721-3 (ISBN)
Conference
Data Compression Conference (DCC), APR 04-07, 2017, Snowbird, UT
Note

QC 20190208

Available from: 2019-02-08 Created: 2019-02-08 Last updated: 2019-02-08Bibliographically approved
Wu, H., Li, H. & Flierl, M. (2016). AN EMBEDDED 3D GEOMETRY SCORE FOR MOBILE 3D VISUAL SEARCH. In: 2016 IEEE 18TH INTERNATIONAL WORKSHOP ON MULTIMEDIA SIGNAL PROCESSING (MMSP): . Paper presented at 18th IEEE International Workshop on Multimedia Signal Processing (MMSP), SEP 21-23, 2016, Montreal, CANADA. Institute of Electrical and Electronics Engineers (IEEE)
Open this publication in new window or tab >>AN EMBEDDED 3D GEOMETRY SCORE FOR MOBILE 3D VISUAL SEARCH
2016 (English)In: 2016 IEEE 18TH INTERNATIONAL WORKSHOP ON MULTIMEDIA SIGNAL PROCESSING (MMSP), Institute of Electrical and Electronics Engineers (IEEE), 2016Conference paper, Published paper (Refereed)
Abstract [en]

The scoring function is a central component in mobile visual search. In this paper, we propose an embedded 3D geometry score for mobile 3D visual search (M3DVS). In contrast to conventional mobile visual search, M3DVS uses not only the visual appearance of query objects, but utilizes also the underlying 3D geometry. The proposed scoring function interprets visual search as a process that reduces uncertainty among candidate objects when observing a query. For M3DVS, the uncertainty is reduced by both appearance-based visual similarity and 3D geometric similarity. For the latter, we give an algorithm for estimating the query-dependent threshold for geometric similarity. In contrast to visual similarity, the threshold for geometric similarity is relative due to the constraints of image-based 3D reconstruction. The experimental results show that the embedded 3D geometry score improves the recall-datarate performance when compared to a conventional visual score or 3D geometry-based re-ranking.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2016
Series
IEEE International Workshop on Multimedia Signal Processing, ISSN 2163-3517
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:kth:diva-202670 (URN)10.1109/MMSP.2016.7813366 (DOI)000393302100034 ()2-s2.0-85013157698 (Scopus ID)978-1-5090-3724-7 (ISBN)
Conference
18th IEEE International Workshop on Multimedia Signal Processing (MMSP), SEP 21-23, 2016, Montreal, CANADA
Note

QC 20170306

Available from: 2017-03-06 Created: 2017-03-06 Last updated: 2017-03-06Bibliographically approved
Liu, D. & Flierl, M. (2016). Video coding using multi-reference motion-adaptive transforms based on graphs. In: 2016 IEEE 12th Image, Video, and Multidimensional Signal Processing Workshop, IVMSP 2016: . Paper presented at 12th IEEE Image, Video, and Multidimensional Signal Processing Workshop, IVMSP 2016, 11 July 2016 through 12 July 2016. IEEE
Open this publication in new window or tab >>Video coding using multi-reference motion-adaptive transforms based on graphs
2016 (English)In: 2016 IEEE 12th Image, Video, and Multidimensional Signal Processing Workshop, IVMSP 2016, IEEE, 2016Conference paper, Published paper (Refereed)
Abstract [en]

The purpose of the work is to produce jointly coded frames for efficient video coding. We use motion-adaptive transforms in the temporal domain to generate the temporal subbands. The motion information is used to form graphs for transform construction. In our previous work, the motion-adaptive transform allows only one reference pixel to be the lowband coefficient. In this paper, we extend the motion-adaptive transform such that it permits multiple references and produces multiple lowband coefficients, which can be used in the case of bidirectional or multihypothesis motion estimation. The multi-reference motion-adaptive transform (MRMAT) is always orthonormal, thus, the energy is preserved by the transform. We compare MRMAT and the motion-compensated orthogonal transform (MCOT) [1], while HEVC intra coding is used to encode the temporal subbands. The experimental results show that MRMAT outperforms MCOT by about 0.6dB.

Place, publisher, year, edition, pages
IEEE, 2016
Keywords
Motion-adaptive transform, motioninherited graph, subspace constraint, Codes (symbols), Image processing, Motion analysis, Motion compensation, Motion estimation, Signal processing, Motion information, Multi-hypothesis, Multiple references, Orthogonal transforms, Reference pixels, Video signal processing
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
urn:nbn:se:kth:diva-202176 (URN)10.1109/IVMSPW.2016.7528191 (DOI)000392266500017 ()2-s2.0-84991798781 (Scopus ID)9781509019298 (ISBN)
Conference
12th IEEE Image, Video, and Multidimensional Signal Processing Workshop, IVMSP 2016, 11 July 2016 through 12 July 2016
Funder
Swedish Research Council, 2011-5841
Note

QC 20170228

Available from: 2017-02-28 Created: 2017-02-28 Last updated: 2018-01-13Bibliographically approved
Liu, D. & Flierl, M. (2015). Energy Compaction on Graphs for Motion-Adaptive Transforms. In: Data Compression Conference Proceedings: . Paper presented at 2015 Data Compression Conference, DCC 2015; Snowbird; United States; 7 April 2015 through 9 April 2015 (pp. 457).
Open this publication in new window or tab >>Energy Compaction on Graphs for Motion-Adaptive Transforms
2015 (English)In: Data Compression Conference Proceedings, 2015, p. 457-Conference paper, Published paper (Refereed)
Abstract [en]

It is well known that the Karhunen-Loeve Transform (KLT) diagonalizes the covariance matrix and gives the optimal energy compaction. Since the real covariance matrix may not be obtained in video compression, we consider a covariance model that can be constructed without extra cost. In this work, a covariance model based on a graph is considered for temporal transforms of videos. The relation between the covariance matrix and the Laplacian is studied. We obtain an explicit expression of the relation for tree graphs, where the trees are defined by motion information. The proposed graph-based covariance is a good model for motion-compensated image sequences. In terms of energy compaction, our graph-based covariance model has the potential to outperform the classical Laplacian-based signal analysis.

Series
Data Compression Conference, ISSN 1068-0314
Keywords
Compaction, Covariance matrix, Forestry, Graphic methods, Image coding, Laplace transforms, Mathematical transformations, Matrix algebra, Principal component analysis, Trees (mathematics), Covariance modeling, Energy compaction, Image sequence, Karhunen Loeve Transform (KLT), Motion information, Motion-adaptive transform, Optimal energy, Temporal transforms, Data compression, Data Transmission, Energy, Mathematical Models
National Category
Computer Systems Probability Theory and Statistics
Identifiers
urn:nbn:se:kth:diva-174701 (URN)10.1109/DCC.2015.86 (DOI)000380409800064 ()2-s2.0-84938930606 (Scopus ID)10.1109/DCC.2015.86 (ISBN)
Conference
2015 Data Compression Conference, DCC 2015; Snowbird; United States; 7 April 2015 through 9 April 2015
Note

QC 20151111

Available from: 2015-11-11 Created: 2015-10-07 Last updated: 2016-08-23Bibliographically approved
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Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-7807-5681

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