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An Implementation Of A Rate Controller Using A Neural Network

Sundeqvist, Martin LU (2018) In Master's Theses in Mathematical Sciences FMAM05 20181
Mathematics (Faculty of Engineering)
Abstract
The rate controller is a crucial component of a video encoder. The component regulates bit rate in a video, determining how much information is to be updated in a video frame by assigning an overall compression rate to said frame. At Axis Communications, a company specializing in network cameras, the component is implemented using traditional automatic control methods operating on a select few data parameters. This thesis project aims to implement the rate controller with a machine learning approach, using a neural network. A key challenge is the task of devising a set of customized quality measurements suited for the purposes of surveillance video. Mainly, these measurements are based around object detection. The quality measurement... (More)
The rate controller is a crucial component of a video encoder. The component regulates bit rate in a video, determining how much information is to be updated in a video frame by assigning an overall compression rate to said frame. At Axis Communications, a company specializing in network cameras, the component is implemented using traditional automatic control methods operating on a select few data parameters. This thesis project aims to implement the rate controller with a machine learning approach, using a neural network. A key challenge is the task of devising a set of customized quality measurements suited for the purposes of surveillance video. Mainly, these measurements are based around object detection. The quality measurement deemed most promising for future use is based on the presence of false positives and negatives in object detection data. The measurements are used to generate a set of labels, each a theoretically optimal compression rate. The input features used by the network is video meta data, a set of parameters describing video content that is more low-dimensional than pixel data from the video. An attempt at encoding videos with a rate controller implemented using a set of trained neural networks is finally carried out. The results indicate that the networks can be trained to adapt compression rate based on changes in activity within a video. However, the compression rates given by the network can change drastically from frame-to-frame. Also, the ability of the networks to adapt to smaller changes can not be reliably determined, requiring further testing. (Less)
Please use this url to cite or link to this publication:
author
Sundeqvist, Martin LU
supervisor
organization
course
FMAM05 20181
year
type
H2 - Master's Degree (Two Years)
subject
publication/series
Master's Theses in Mathematical Sciences
report number
LUTFMA-3367-2018
ISSN
1404-6342
other publication id
2018:E70
language
English
id
8963029
date added to LUP
2018-12-06 14:34:33
date last changed
2018-12-06 14:34:33
@misc{8963029,
  abstract     = {The rate controller is a crucial component of a video encoder. The component regulates bit rate in a video, determining how much information is to be updated in a video frame by assigning an overall compression rate to said frame. At Axis Communications, a company specializing in network cameras, the component is implemented using traditional automatic control methods operating on a select few data parameters. This thesis project aims to implement the rate controller with a machine learning approach, using a neural network. A key challenge is the task of devising a set of customized quality measurements suited for the purposes of surveillance video. Mainly, these measurements are based around object detection. The quality measurement deemed most promising for future use is based on the presence of false positives and negatives in object detection data. The measurements are used to generate a set of labels, each a theoretically optimal compression rate. The input features used by the network is video meta data, a set of parameters describing video content that is more low-dimensional than pixel data from the video. An attempt at encoding videos with a rate controller implemented using a set of trained neural networks is finally carried out. The results indicate that the networks can be trained to adapt compression rate based on changes in activity within a video. However, the compression rates given by the network can change drastically from frame-to-frame. Also, the ability of the networks to adapt to smaller changes can not be reliably determined, requiring further testing.},
  author       = {Sundeqvist, Martin},
  issn         = {1404-6342},
  language     = {eng},
  note         = {Student Paper},
  series       = {Master's Theses in Mathematical Sciences},
  title        = {An Implementation Of A Rate Controller Using A Neural Network},
  year         = {2018},
}