Please use this identifier to cite or link to this item: http://localhost/handle/Hannan/645276
Title: Blind Picture Upscaling Ratio Prediction
Authors: Todd R. Goodall;Ioannis Katsavounidis;Zhi Li;Anne Aaron;Alan C. Bovik
subject: upscaling prediction|upscaling detection|Natural scene statistics
Year: 2016
Publisher: IEEE
Abstract: Natural scene statistics are well studied in the context of picture quality assessment and have been used in a wide variety of top-performing picture quality prediction models. Upscaling artifacts have been measured with regards to quality impairment using these kinds of models. However, the assessment and classification of subtle, less discriminable upscaling artifacts remains an unsolved problem. The nearly imperceptible artifacts pertaining to the extent and type of upscaling have not been predicted using natural scene statistics (NSS)-based models. We develop an accurate model for predicting the upscaling ratio applied to any natural image. By decomposing an input image frame using an orthogonal filter bank and locally normalizing the resulting responses, we show that the local energy terms can be used to predict the upscaling ratio. In fact, a simple linear regressor can be trained on these energy measurements; hence, no hyperparameter tuning is necessary. We compare the proposed model with other no-reference models using real-world data contained in the Netflix collection.
URI: http://localhost/handle/Hannan/177962
http://localhost/handle/Hannan/645276
ISSN: 1070-9908
1558-2361
volume: 23
issue: 12
Appears in Collections:2016

Files in This Item:
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Title: Blind Picture Upscaling Ratio Prediction
Authors: Todd R. Goodall;Ioannis Katsavounidis;Zhi Li;Anne Aaron;Alan C. Bovik
subject: upscaling prediction|upscaling detection|Natural scene statistics
Year: 2016
Publisher: IEEE
Abstract: Natural scene statistics are well studied in the context of picture quality assessment and have been used in a wide variety of top-performing picture quality prediction models. Upscaling artifacts have been measured with regards to quality impairment using these kinds of models. However, the assessment and classification of subtle, less discriminable upscaling artifacts remains an unsolved problem. The nearly imperceptible artifacts pertaining to the extent and type of upscaling have not been predicted using natural scene statistics (NSS)-based models. We develop an accurate model for predicting the upscaling ratio applied to any natural image. By decomposing an input image frame using an orthogonal filter bank and locally normalizing the resulting responses, we show that the local energy terms can be used to predict the upscaling ratio. In fact, a simple linear regressor can be trained on these energy measurements; hence, no hyperparameter tuning is necessary. We compare the proposed model with other no-reference models using real-world data contained in the Netflix collection.
URI: http://localhost/handle/Hannan/177962
http://localhost/handle/Hannan/645276
ISSN: 1070-9908
1558-2361
volume: 23
issue: 12
Appears in Collections:2016

Files in This Item:
File Description SizeFormat 
7553480.pdf588.85 kBAdobe PDFThumbnail
Preview File
Title: Blind Picture Upscaling Ratio Prediction
Authors: Todd R. Goodall;Ioannis Katsavounidis;Zhi Li;Anne Aaron;Alan C. Bovik
subject: upscaling prediction|upscaling detection|Natural scene statistics
Year: 2016
Publisher: IEEE
Abstract: Natural scene statistics are well studied in the context of picture quality assessment and have been used in a wide variety of top-performing picture quality prediction models. Upscaling artifacts have been measured with regards to quality impairment using these kinds of models. However, the assessment and classification of subtle, less discriminable upscaling artifacts remains an unsolved problem. The nearly imperceptible artifacts pertaining to the extent and type of upscaling have not been predicted using natural scene statistics (NSS)-based models. We develop an accurate model for predicting the upscaling ratio applied to any natural image. By decomposing an input image frame using an orthogonal filter bank and locally normalizing the resulting responses, we show that the local energy terms can be used to predict the upscaling ratio. In fact, a simple linear regressor can be trained on these energy measurements; hence, no hyperparameter tuning is necessary. We compare the proposed model with other no-reference models using real-world data contained in the Netflix collection.
URI: http://localhost/handle/Hannan/177962
http://localhost/handle/Hannan/645276
ISSN: 1070-9908
1558-2361
volume: 23
issue: 12
Appears in Collections:2016

Files in This Item:
File Description SizeFormat 
7553480.pdf588.85 kBAdobe PDFThumbnail
Preview File