19,111 research outputs found
Analysis of affine motion-compensated prediction and its application in aerial video coding
Motion-compensated prediction is used in video coding standards like High Efficiency Video Coding (HEVC) as one key element of data compression. Commonly, a purely translational motion model is employed. In order to also cover non-translational motion types like rotation or scaling (zoom) contained in aerial video sequences such as captured from unmanned aerial vehicles, an affine motion model can be applied. In this work, a model for affine motion-compensated prediction in video coding is derived by extending a model of purely translational motion-compensated prediction. Using the rate-distortion theory and the displacement estimation error caused by inaccurate affine motion parameter estimation, the minimum required bit rate for encoding the prediction error is determined. In this model, the affine transformation parameters are assumed to be affected by statistically independent estimation errors, which all follow a zero-mean Gaussian distributed probability density function (pdf). The joint pdf of the estimation errors is derived and transformed into the pdf of the location-dependent displacement estimation error in the image. The latter is related to the minimum required bit rate for encoding the prediction error. Similar to the derivations of the fully affine motion model, a four-parameter simplified affine model is investigated. It is of particular interest since such a model is considered for the upcoming video coding standard Versatile Video Coding (VVC) succeeding HEVC. As the simplified affine motion model is able to describe most motions contained in aerial surveillance videos, its application in video coding is justified. Both models provide valuable information about the minimum bit rate for encoding the prediction error as a function of affine estimation accuracies. Although the bit rate in motion-compensated prediction can be considerably reduced by using a motion model which is able to describe motion types occurring in the scene, the total video bit rate may remain quite high, depending on the motion estimation accuracy. Thus, at the example of aerial surveillance sequences, a codec independent region of interest- ( ROI -) based aerial video coding system is proposed that exploits the characteristic of such sequences. Assuming the captured scene to be planar, one frame can be projected into another using global motion compensation. Consequently, only new emerging areas have to be encoded. At the decoder, all new areas are registered into a so-called mosaic. From this, reconstructed frames are extracted and concatenated as a video sequence. To also preserve moving objects in the reconstructed video, local motion is detected and encoded in addition to the new areas. The proposed general ROI coding system was evaluated for very low and low bit rates between 100 and 5000 kbit/s for aerial sequences of HD resolution. It is able to reduce the bit rate by 90% compared to common HEVC coding of similar quality. Subjective tests confirm that the overall image quality of the ROI coding system exceeds that of a common HEVC encoder especially at very low bit rates below 1 Mbit/s. To prevent discontinuities introduced by inaccurate global motion estimation, as may be caused by radial lens distortion, a fully automatic in-loop radial distortion compensation is proposed. For this purpose, an unknown radial distortion compensation parameter that is constant for a group of frames is jointly estimated with the global motion. This parameter is optimized to minimize the distortions of the projections of frames in the mosaic. By this approach, the global motion compensation was improved by 0.27dB and discontinuities in the frames extracted from the mosaic are diminished. As an additional benefit, the generation of long-term mosaics becomes possible, constructed by more than 1500 aerial frames with unknown radial lens distortion and without any calibration or manual lens distortion compensation.Bewegungskompensierte Prädiktion wird in Videocodierstandards wie High Efficiency Video Coding (HEVC) als ein Schlüsselelement zur Datenkompression verwendet. Typischerweise kommt dabei ein rein translatorisches Bewegungsmodell zum
Einsatz. Um auch nicht-translatorische Bewegungen wie Rotation oder Skalierung
(Zoom) beschreiben zu können, welche beispielsweise in von unbemannten Luftfahrzeugen aufgezeichneten Luftbildvideosequenzen enthalten sind, kann ein affines Bewegungsmodell verwendet werden. In dieser Arbeit wird aufbauend auf einem rein translatorischen Bewegungsmodell ein Modell für affine bewegungskompensierte Prädiktion hergeleitet. Unter Verwendung der Raten-Verzerrungs-Theorie und des Verschiebungsschätzfehlers, welcher aus einer inexakten affinen Bewegungsschätzung resultiert, wird die minimal erforderliche Bitrate zur Codierung des Prädiktionsfehlers hergeleitet. Für die Modellierung wird angenommen, dass die sechs Parameter einer affinen Transformation durch statistisch unabhängige Schätzfehler gestört sind. Für jeden dieser Schätzfehler
wird angenommen, dass die Wahrscheinlichkeitsdichteverteilung einer mittelwertfreien Gaußverteilung entspricht. Aus der Verbundwahrscheinlichkeitsdichte der Schätzfehler wird die Wahrscheinlichkeitsdichte des ortsabhängigen Verschiebungsschätzfehlers im Bild berechnet. Letztere wird schließlich zu der minimalen Bitrate in Beziehung gesetzt, welche für die Codierung des Prädiktionsfehlers benötigt wird. Analog zur obigen Ableitung des Modells für das voll-affine Bewegungsmodell wird ein vereinfachtes affines Bewegungsmodell mit vier Freiheitsgraden untersucht. Ein solches Modell wird derzeit auch im Rahmen der Standardisierung des HEVC-Nachfolgestandards Versatile Video Coding (VVC) evaluiert. Da das vereinfachte Modell bereits die meisten in Luftbildvideosequenzen vorkommenden Bewegungen abbilden kann, ist der Einsatz des vereinfachten affinen Modells in der Videocodierung gerechtfertigt. Beide Modelle liefern wertvolle Informationen über die minimal benötigte Bitrate zur Codierung des Prädiktionsfehlers in Abhängigkeit von der affinen Schätzgenauigkeit. Zwar kann die Bitrate mittels bewegungskompensierter Prädiktion durch Wahl eines geeigneten Bewegungsmodells und akkurater affiner Bewegungsschätzung stark
reduziert werden, die verbleibende Gesamtbitrate kann allerdings dennoch relativ hoch sein. Deshalb wird am Beispiel von Luftbildvideosequenzen ein Regionen-von-Interesse- (ROI-) basiertes Codiersystem vorgeschlagen, welches spezielle Eigenschaften solcher Sequenzen ausnutzt. Unter der Annahme, dass eine aufgenommene Szene planar ist, kann ein Bild durch globale Bewegungskompensation in ein anderes projiziert werden. Deshalb müssen vom aktuellen Bild prinzipiell nur noch neu im Bild erscheinende Bereiche codiert werden. Am Decoder werden alle neuen Bildbereiche in einem gemeinsamen Mosaikbild registriert, aus dem schließlich die Einzelbilder der Videosequenz rekonstruiert werden können. Um auch lokale Bewegungen abzubilden, werden bewegte Objekte detektiert und zusätzlich zu neuen Bildbereichen als ROI codiert. Die Leistungsfähigkeit des ROI-Codiersystems wurde insbesondere für sehr niedrige und niedrige Bitraten von 100 bis 5000 kbit/s für Bilder in HD-Auflösung evaluiert. Im Vergleich zu einer gewöhnlichen HEVC-Codierung kann die Bitrate um 90% reduziert werden. Durch subjektive Tests wurde bestätigt, dass das ROI-Codiersystem insbesondere für sehr niedrige Bitraten von unter 1 Mbit/s deutlich leistungsfähiger in Bezug auf Detailauflösung und Gesamteindruck ist als ein herkömmliches HEVC-Referenzsystem. Um Diskontinuitäten in den rekonstruierten Videobildern zu vermeiden, die durch eine durch Linsenverzeichnungen induzierte ungenaue globale Bewegungsschätzung entstehen können, wird eine automatische Radialverzeichnungskorrektur vorgeschlagen. Dabei wird ein unbekannter, jedoch über mehrere Bilder konstanter Korrekturparameter gemeinsam mit der globalen Bewegung geschätzt. Dieser Parameter wird derart optimiert, dass die Projektionen der Bilder in das Mosaik möglichst wenig verzerrt werden. Daraus resultiert eine um 0,27dB verbesserte globale Bewegungskompensation, wodurch weniger Diskontinuitäten in den aus dem Mosaik rekonstruierten Bildern entstehen. Dieses Verfahren ermöglicht zusätzlich die Erstellung von Langzeitmosaiken aus über 1500 Luftbildern mit unbekannter Radialverzeichnung und ohne manuelle Korrektur
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Mobile Audiovisual Terminal: System Design and Subjective Testing in DECT and UMTS networks
It is anticipated that there will shortly be a requirement
for multimedia terminals that operate via mobile
communications systems. This paper presents a functional specification
for such a terminal operating at 32 kb/s in a digital
European cordless telecommunications (DECT) and universal
mobile telecommunications system (UMTS) radio network. A terminal
has been built, based on a PC with digital signal processor
(DSP) boards for audio and video coding and decoding. Speech
coding is by a phonetically driven code-excited linear prediction
(CELP) speech coder and video coding by a block-oriented hybrid
discrete cosine transform (DCT) coder. Separate channel coding
is provided for the audio and video data. The paper describes the
techniques used for audio and video coding, channel coding, and
synchronization. Methods of subjective testing in a DECT network
and in a UMTS network are also described. These consisted of
subjective tests of first impressions of the mobile audio–visual
terminal (MAVT) quality, interactive tests, and the completion
of an exit questionnaire. The test results showed that the quality
of the audio was sufficiently good for comprehension and the
video was sufficiently good for following and repeating simple
mechanical tasks. However, the quality of the MAVT was not
good enough for general use where high-quality audio and video
was needed, especially when transmission was in a noisy radio
environment
Mitigation of H.264 and H.265 Video Compression for Reliable PRNU Estimation
The photo-response non-uniformity (PRNU) is a distinctive image sensor
characteristic, and an imaging device inadvertently introduces its sensor's
PRNU into all media it captures. Therefore, the PRNU can be regarded as a
camera fingerprint and used for source attribution. The imaging pipeline in a
camera, however, involves various processing steps that are detrimental to PRNU
estimation. In the context of photographic images, these challenges are
successfully addressed and the method for estimating a sensor's PRNU pattern is
well established. However, various additional challenges related to generation
of videos remain largely untackled. With this perspective, this work introduces
methods to mitigate disruptive effects of widely deployed H.264 and H.265 video
compression standards on PRNU estimation. Our approach involves an intervention
in the decoding process to eliminate a filtering procedure applied at the
decoder to reduce blockiness. It also utilizes decoding parameters to develop a
weighting scheme and adjust the contribution of video frames at the macroblock
level to PRNU estimation process. Results obtained on videos captured by 28
cameras show that our approach increases the PRNU matching metric up to more
than five times over the conventional estimation method tailored for photos
MASCOT : metadata for advanced scalable video coding tools : final report
The goal of the MASCOT project was to develop new video coding schemes and tools that provide both an increased coding efficiency as well as extended scalability features compared to technology that was available at the beginning of the project. Towards that goal the following tools would be used: - metadata-based coding tools; - new spatiotemporal decompositions; - new prediction schemes. Although the initial goal was to develop one single codec architecture that was able to combine all new coding tools that were foreseen when the project was formulated, it became clear that this would limit the selection of the new tools. Therefore the consortium decided to develop two codec frameworks within the project, a standard hybrid DCT-based codec and a 3D wavelet-based codec, which together are able to accommodate all tools developed during the course of the project
Downlink Video Streaming for Users Non-Equidistant from Base Station
We consider multiuser video transmission for users that are non-equidistantly positioned from base station. We propose a greedy algorithm for video streaming in a wireless system with capacity achieving channel coding, that implements the cross-layer principle by partially separating the physical and the application layer. In such a system the parameters at the physical layer are dependent on the packet length and the conditions in the wireless channel and the parameters at the application layer are dependent on the reduction of the expected distortion assuming no packet errors in the system. We also address the fairness in the multiuser video system with non-equidistantly positioned users. Our fairness algorithm is based on modified opportunistic round robin scheduling. We evaluate the performance of the proposed algorithms by simulating the transmission of H.264/AVC video signals in a TDMA wireless system
Video Classification With CNNs: Using The Codec As A Spatio-Temporal Activity Sensor
We investigate video classification via a two-stream convolutional neural
network (CNN) design that directly ingests information extracted from
compressed video bitstreams. Our approach begins with the observation that all
modern video codecs divide the input frames into macroblocks (MBs). We
demonstrate that selective access to MB motion vector (MV) information within
compressed video bitstreams can also provide for selective, motion-adaptive, MB
pixel decoding (a.k.a., MB texture decoding). This in turn allows for the
derivation of spatio-temporal video activity regions at extremely high speed in
comparison to conventional full-frame decoding followed by optical flow
estimation. In order to evaluate the accuracy of a video classification
framework based on such activity data, we independently train two CNN
architectures on MB texture and MV correspondences and then fuse their scores
to derive the final classification of each test video. Evaluation on two
standard datasets shows that the proposed approach is competitive to the best
two-stream video classification approaches found in the literature. At the same
time: (i) a CPU-based realization of our MV extraction is over 977 times faster
than GPU-based optical flow methods; (ii) selective decoding is up to 12 times
faster than full-frame decoding; (iii) our proposed spatial and temporal CNNs
perform inference at 5 to 49 times lower cloud computing cost than the fastest
methods from the literature.Comment: Accepted in IEEE Transactions on Circuits and Systems for Video
Technology. Extension of ICIP 2017 conference pape
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Error resilient video transcoding for robust inter-network communications using GPRS
A novel fully comprehensive mobile video communications
system is proposed in this paper. This system exploits
the useful rate management features of the video transcoders and
combines them with error resilience for transmissions of coded
video streams over general packet radio service (GPRS) mobileaccess
networks. The error-resilient video transcoding operation
takes place at a centralized point, referred to as a video proxy,
which provides the necessary output transmission rates with the
required amount of robustness. With the use of this proposed
algorithm, error resilience can be added to an already compressed
video stream at an intermediate stage at the edge of two or more
different networks through two resilience schemes, namely the
adaptive intra refresh (AIR) and feedback control signaling (FCS)
methods. Both resilience tools impose an output rate increase
which can also be prevented with the proposed novel technique in
this paper. Thus, an error-resilient video transcoding scheme is
presented to give robust video outputs at near target transmission
rates that only require the same number of GPRS timeslots as
the nonresilient schemes. Moreover, an ultimate robustness is
also accomplished with the combination of the two resilience
algorithms at the video proxy. Extensive computer simulations
demonstrate the effectiveness of the proposed system
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