Image quality assessment using adaptive non-overlapping mean estimation
First Claim
1. An apparatus to perform image quality assessment, the apparatus comprising:
- a training module to train a neural network, based on a set of training images, to classify quality of a first image;
an image determiner to replace respective blocks of pixels of the first image with mean values of the respective blocks of pixels to determine a second image having a smaller size than the first image;
a feature extractor to determine a vector of features including a blur feature value for the second image and to apply the vector of features to the neural network after the neural network has been trained, the feature extractor to determine the blur feature value based on an amount the second image differs from a version of the second image filtered with a blurring filter in horizontal and vertical directions of the second image; and
an image classifier to classify a quality of the first image based on an output of the neural network, the training module, the image determiner, the feature extractor, and the image classifier implemented by hardware circuitry or at least one processor.
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Abstract
Methods, apparatus, systems and articles of manufacture (e.g., physical storage media) to assess image quality using adaptive non-overlapping mean estimation are disclosed. Example image quality assessment methods disclosed herein include replacing respective blocks of pixels of a first image with mean values of the respective blocks of pixels to determine a second image having a smaller size than the first image. Disclosed example image quality assessment methods also include determining a vector of features for the second image. Disclosed example image quality assessment methods further include applying the vector of features to a neural network, and classifying a quality of the first image based on an output of the neural network.
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Citations
20 Claims
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1. An apparatus to perform image quality assessment, the apparatus comprising:
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a training module to train a neural network, based on a set of training images, to classify quality of a first image; an image determiner to replace respective blocks of pixels of the first image with mean values of the respective blocks of pixels to determine a second image having a smaller size than the first image; a feature extractor to determine a vector of features including a blur feature value for the second image and to apply the vector of features to the neural network after the neural network has been trained, the feature extractor to determine the blur feature value based on an amount the second image differs from a version of the second image filtered with a blurring filter in horizontal and vertical directions of the second image; and an image classifier to classify a quality of the first image based on an output of the neural network, the training module, the image determiner, the feature extractor, and the image classifier implemented by hardware circuitry or at least one processor. - View Dependent Claims (2, 3, 4, 5, 6)
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7. An image quality assessment method comprising:
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training, by executing an instruction with a processor, a neural network, based on a set of training images, to classify quality of a first image; replacing, by executing an instruction with the processor, respective blocks of pixels of the first image with mean values of the respective blocks of pixels to determine a second image having a smaller size than the first image; determining, by executing an instruction with the processor, a vector of features including a blur feature value for the second image, the blur feature value determined based on an amount the second image differs from a version of the second image filtered with a blurring filter in horizontal and vertical directions of the second image; applying the vector of features to the neural network after the neural network has been trained; and classifying, by executing an instruction with the processor, a quality of the first image based on an output of the neural network. - View Dependent Claims (8, 9, 10, 11, 12, 13)
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14. A tangible computer readable storage medium comprising computer readable instructions that, when executed, cause a processor to at least:
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train a neural network, based on a set of training images, to classify quality of a first image; replace respective blocks of pixels of the first image with mean values of the respective blocks of pixels to determine a second image having a smaller size than the first image; determine a vector of features including a blur feature value for the second image, the blur feature value determined based on an amount the second image differs from a version of the second image filtered with a blurring filter in horizontal and vertical directions of the second image; apply the vector of features to the neural network after the neural network has been trained; and classify a quality of the first image based on an output of the neural network. - View Dependent Claims (15, 16, 17, 18, 19, 20)
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Specification