<p>This thesis introduces a coding scheme for very low bitrate video coding through the aid of principal component analysis. Principal information of the facial mimic for a person can be extracted and stored in an Eigenspace. Entire video frames of this persons face can then be compressed with the Eigenspace to only a few projection coefficients. Principal component video coding encodes entire frames at once and increased frame size does not increase the necessary bitrate for encoding, as standard coding schemes do. This enables video communication with high frame rate, spatial resolution and visual quality at very low bitrates. No standard video coding technique provides these four features at the same time.</p><p>Theoretical bounds for using principal components to encode facial video sequences are presented. Two different theoretical bounds are derived. One that describes the minimal distortion when a certain number of Eigenimages are used and one that describes the minimum distortion when a minimum number of bits are used.</p><p>We investigate how the reconstruction quality for the coding scheme is affected when the Eigenspace, mean image and coefficients are compressed to enable efficient transmission. The Eigenspace and mean image are compressed through JPEG-compression while the while the coefficients are quantized. We show that high compression ratios can be used almost without any decrease in reconstruction quality for the coding scheme.</p><p>Different ways of re-using the Eigenspace for a person extracted from one video sequence to encode other video sequences are examined. The most important factor is the positioning of the facial features in the video frames.</p><p>Through a user test we find that it is extremely important to consider secondary workloads and how users make use of video when experimental setups are designed.</p>

This thesis introduces a coding scheme for very low bitrate video coding through the aid of principal component analysis. Principal information of the facial mimic for a person can be extracted and stored in an Eigenspace. Entire video frames of this persons face can then be compressed with the Eigenspace to only a few projection coefficients. Principal component video coding encodes entire frames at once and increased frame size does not increase the necessary bitrate for encoding, as standard coding schemes do. This enables video communication with high frame rate, spatial resolution and visual quality at very low bitrates. No standard video coding technique provides these four features at the same time. Theoretical bounds for using principal components to encode facial video sequences are presented. Two different theoretical bounds are derived. One that describes the minimal distortion when a certain number of Eigenimages are used and one that describes the minimum distortion when a minimum number of bits are used. We investigate how the reconstruction quality for the coding scheme is affected when the Eigenspace, mean image and coefficients are compressed to enable efficient transmission. The Eigenspace and mean image are compressed through JPEG-compression while the while the coefficients are quantized. We show that high compression ratios can be used almost without any decrease in reconstruction quality for the coding scheme. Different ways of re-using the Eigenspace for a person extracted from one video sequence to encode other video sequences are examined. The most important factor is the positioning of the facial features in the video frames. Through a user test we find that it is extremely important to consider secondary workloads and how users make use of video when experimental setups are designed.

A large portion of network resources is consumed by video traffic, and this increasing demand causes challenges in the area of video coding. As the expectations for high-quality video grow the required bitrate rises in accordance, which motivates research on lowering bitrates. This research investigated the question: How can genre-based video transcoding (using x265) be improved for animated Video On Demand-content, to maintain the subjective quality of the current profile at a lower bitrate? A transcoding profile for animated content was created with an improved motion estimation technique and an experimental change of AQ mode. A double-blind AB test was conducted where the users were exposed to 10 video pairs transcoded with the old and the new transcoding profile and chose their preference. 570 people participated. Results show that for most instances people barely saw any difference between the profiles, but a small difference in perceived quality for 2D and 3D content. Consequently, it appears that there is a potential for reducing the bitrate for animated content but further exploration is required to draw statistically significant conclusions. / En stor del av nätverksresurserna används till videotrafik, och denna ökande efterfrågan medför utmaningar inom området videkodning. När förväntningarna på högkvalitativ video ökar stiger den krävda bithastigheten i enlighet, vilket motiverar forskning kring låga bithastigheter. Denna forskning undersökte följande fråga: Hur kan genrebaserad videotranskodering (med hjälp av x265) förbättras för animerat Video On Demand-innehåll, för att bibehålla den subjektiva kvaliteten hos den nuvarande profilen med en lägre bithastighet? En transkoderingsprofil för animerat innehåll skapades med en förbättrad motion estimation-teknik och en experimentell förändring av AQ-läge. En dubbelblint AB-test genomfördes där användarna exponerades för 10 videopar transkoderade med den gamla och den nya profilen, och valde sin preferens. 570 personer deltog. Resultaten visade att för de flesta instanserna såg deltagarna knappt någon skillnad mellan profilerna, men en viss skillnad i upplevd kvalitet för 2D- och 3D-material. Följaktligen verkar det som om det finns potential att minska bithastigheten för animerat innehåll, men att detta måste undersökas ytterligare för att dra statistiskt signifikanta slutsatser.

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.