Reference number : GC-2017-Q1-00384, amendment number : , agreement type : G, recipient legal name : Shirani, Shahram (McMaster University), recipient country : CA, recipient province : nan, recipient city : Ontario, recipient postal code : nan, program name english : Engage Grants for Universities, program name french : Subventions d'engagement partenarial pour les universités, program purpose english : Digital video requires a huge volume of data and must be compressed before it can be stored and/or_x000D_
transmitted. TV broadcasters, video game manufacturers, video content distributors use video compression in_x000D_
their products and/or services. AMD designs and manufactures graphics cards and microprocessors for tablets,_x000D_
gaming consoles, embedded devices and cloud servers. These products must yield the best possible video_x000D_
quality. In video compression, there is an inherent trade-off between bitrate and video quality. Obtaining the_x000D_
best video quality for a given bitrate is, therefore, a crucial task. The bitrate of a video sequence can be reduced_x000D_
by reducing its resolution prior to encoding or by using a larger quantization parameter during encoding. Which_x000D_
of these two options yields less quality loss depends on the video content as well as the available network_x000D_
bandwidth. The goal of this research project is to design a machine learning algorithm to make a run-time_x000D_
decision of whether to encode a video picture (or group of pictures) at the original high resolution, or to reduce_x000D_
resolution, encode the lower resolution version with a smaller quantization step, decode and upsample at_x000D_
receiver side with expectation to achieve the best quality for a given bit rate. By utilizing the optimal_x000D_
resolution/quantization step combination, our developed adaptive video resolution adjustment scheme can_x000D_
result in significant bitrate savings for a target quality or significant quality improvements for a target bitrate., program purpose french : Digital video requires a huge volume of data and must be compressed before it can be stored and/or_x000D_
transmitted. TV broadcasters, video game manufacturers, video content distributors use video compression in_x000D_
their products and/or services. AMD designs and manufactures graphics cards and microprocessors for tablets,_x000D_
gaming consoles, embedded devices and cloud servers. These products must yield the best possible video_x000D_
quality. In video compression, there is an inherent trade-off between bitrate and video quality. Obtaining the_x000D_
best video quality for a given bitrate is, therefore, a crucial task. The bitrate of a video sequence can be reduced_x000D_
by reducing its resolution prior to encoding or by using a larger quantization parameter during encoding. Which_x000D_
of these two options yields less quality loss depends on the video content as well as the available network_x000D_
bandwidth. The goal of this research project is to design a machine learning algorithm to make a run-time_x000D_
decision of whether to encode a video picture (or group of pictures) at the original high resolution, or to reduce_x000D_
resolution, encode the lower resolution version with a smaller quantization step, decode and upsample at_x000D_
receiver side with expectation to achieve the best quality for a given bit rate. By utilizing the optimal_x000D_
resolution/quantization step combination, our developed adaptive video resolution adjustment scheme can_x000D_
result in significant bitrate savings for a target quality or significant quality improvements for a target bitrate., agreement title english : Optimizing Video Quality Using Machine-Learning-Controlled Adaptive Resolution, Video Compression, agreement title french : Optimizing Video Quality Using Machine-Learning-Controlled Adaptive Resolution, Video Compression, agreement number : EGP, agreement value : 25000.0, agreement start date : 2017-06-14, agreement end date : nan, description english : nan, description french : nan, expected results english : nan, expected results french : nan, additional information english : Grant or Award spanning more than one fiscal year. (2017-2018 to 2018-2019), additional information french : Subvention ou bourse octroyée s'appliquant à plus d'un exercice financier. (2017-2018 à 2018-2019), owner organization : nserc-crsng, owner organization title : Natural Sciences and Engineering Research Council of Canada | Conseil de recherches en sciences naturelles et en génie du Canada