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High definition wearable video communication
Umeå University, Faculty of Science and Technology, Department of Applied Physics and Electronics. (Digital Media Lab)
Umeå University, Faculty of Science and Technology, Department of Applied Physics and Electronics. (Digital Media Lab)
2009 (English)In: Image analysis: 16th Scandinavian Conference, SCIA 2009, Oslo, Norway, June 15-18, 2009. Proceedings / [ed] Arnt-Børre Salberg, Jon Yngve Hardeberg, Robert Jenssen, Heidelberg: Springer Berlin , 2009, 500-512 p.Conference paper (Refereed)
Abstract [en]

High definition (HD) video can provide video communication which is as crisp and sharp as face-to-face communication. Wearable video equipment also provide the user with mobility; the freedom to move. HD video requires high bandwidth and yields high encoding and decoding complexity when encoding based on DCT and motion estimation is used. We propose a solution that can drastically lower the bandwidth and complexity for video transmission. Asymmetrical principal component analysis can initially encode HD video into bitrates which are low considering the type of video (< 300 kbps) and after a startup phase the bitrate can be reduced to less than 5 kbps. The complexity for encoding and decoding of this video is very low; something that will save battery power for mobile devices. All of this is done only at the cost of lower quality in frame areas which aren’t considered semantically important.

Place, publisher, year, edition, pages
Heidelberg: Springer Berlin , 2009. 500-512 p.
, Lecture Notes in Computer Science, ISSN 1611-3349 ; 5575
National Category
Physical Sciences
URN: urn:nbn:se:umu:diva-35809DOI: 47833q8x9651j834/ISBN: 978-3-642-02229-6OAI: diva2:349240
Scandinavian Conference on Image Analysis, SCIA
Available from: 2010-09-06 Created: 2010-09-06 Last updated: 2012-06-05Bibliographically approved
In thesis
1. Very Low Bitrate Video Communication: A Principal Component Analysis Approach
Open this publication in new window or tab >>Very Low Bitrate Video Communication: A Principal Component Analysis Approach
2008 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

A large amount of the information in conversations come from non-verbal cues such as facial expressions and body gesture. These cues are lost when we don't communicate face-to-face. But face-to-face communication doesn't have to happen in person. With video communication we can at least deliver information about the facial mimic and some gestures. This thesis is about video communication over distances; communication that can be available over networks with low capacity since the bitrate needed for video communication is low.

A visual image needs to have high quality and resolution to be semantically meaningful for communication. To deliver such video over networks require that the video is compressed. The standard way to compress video images, used by H.264 and MPEG-4, is to divide the image into blocks and represent each block with mathematical waveforms; usually frequency features. These mathematical waveforms are quite good at representing any kind of video since they do not resemble anything; they are just frequency features. But since they are completely arbitrary they cannot compress video enough to enable use over networks with limited capacity, such as GSM and GPRS.

Another issue is that such codecs have a high complexity because of the redundancy removal with positional shift of the blocks. High complexity and bitrate means that a device has to consume a large amount of energy for encoding, decoding and transmission of such video; with energy being a very important factor for battery-driven devices.

Drawbacks of standard video coding mean that it isn't possible to deliver video anywhere and anytime when it is compressed with such codecs. To resolve these issues we have developed a totally new type of video coding. Instead of using mathematical waveforms for representation we use faces to represent faces. This makes the compression much more efficient than if waveforms are used even though the faces are person-dependent.

By building a model of the changes in the face, the facial mimic, this model can be used to encode the images. The model consists of representative facial images and we use a powerful mathematical tool to extract this model; namely principal component analysis (PCA). This coding has very low complexity since encoding and decoding only consist of multiplication operations. The faces are treated as single encoding entities and all operations are performed on full images; no block processing is needed. These features mean that PCA coding can deliver high quality video at very low bitrates with low complexity for encoding and decoding.

With the use of asymmetrical PCA (aPCA) it is possible to use only semantically important areas for encoding while decoding full frames or a different part of the frames.

We show that a codec based on PCA can compress facial video to a bitrate below 5 kbps and still provide high quality. This bitrate can be delivered on a GSM network. We also show the possibility of extending PCA coding to encoding of high definition video.

Place, publisher, year, edition, pages
Umeå: Tillämpad fysik och elektronik, 2008. 90 p.
Digital Media Lab, ISSN 1652-6295 ; 11
Video compression, Very low bitrate, Principal component analysis, Complexity, Semantically important areas, Wearable video
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
urn:nbn:se:umu:diva-1808 (URN)978-91-7264-644-5 (ISBN)
Public defence
2008-09-26, N200, Naturvetarhuset, Umeå universitet, Umeå, 10:00 (English)
Available from: 2008-09-05 Created: 2008-09-05 Last updated: 2010-11-05Bibliographically approved

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