Image Quality Assessment

Abstract

Systems and methods are disclosed for image signal processing. For example, methods may include receiving a first image from a first image sensor; receiving a second image from a second image sensor; stitching the first image and the second image to obtain a stitched image; identifying an image portion of the stitched image that is positioned on a stitching boundary of the stitched image; and inputting the image portion to a machine learning module to obtain a score, wherein the machine learning module has been trained using training data that included image portions labeled to reflect an absence of stitching and image portions labeled to reflect a presence of stitching, wherein the image portions labeled to reflect a presence of stitching included stitching.

1 - 20 . (canceled) 
     
     
         21 . A system comprising:
 a first image sensor configured to capture a first image;   a second image sensor configured to capture a second image; and   a processing apparatus that is configured to:
 receive the first image from the first image sensor; 
 receive the second image from the second image sensor; 
 stitch the first image and the second image to obtain a stitched image; 
 identify an image portion of the stitched image that is positioned on a stitching boundary of the stitched image; and 
 input the image portion to a machine learning module to obtain a score, wherein the machine learning module has been trained using training data that included image portions labeled to reflect an absence of stitching and image portions labeled to reflect a presence of stitching, wherein the image portions labeled to reflect a presence of stitching included stitching boundaries of stitched images. 
   
     
     
         22 . The system of  claim 21 , in which the processing apparatus is configured to:
 identify one or more additional image portions within the stitched image that occur along the stitching boundary of the stitched image; and   input the one or more additional image portions to the machine learning module to obtain one or more additional scores.   
     
     
         23 . The system of  claim 21 , in which the machine learning module comprises a feature extraction submodule that is configured to determine features based on the image portion. 
     
     
         24 . The system of  claim 21 , in which stitching to obtain the stitched image is performed such that individual pixels of the stitched image are respectively based on either the first image or the second image, but not both. 
     
     
         25 . The system of  claim 21 , in which stitching to obtain the stitched image is performed such that individual pixels of the stitched image are respectively based on either the first image or the second image, but not both; and
 in which the image portions of the training data labeled to reflect a presence of stitching included stitching boundaries of stitched images that were stitched without blending.   
     
     
         26 . The system of  claim 21 , in which the image portion is a block of pixels from the stitched image that includes pixels on both sides of the stitching boundary, where the block of pixels has a resolution less than the resolution of the first image. 
     
     
         27 . The system of  claim 21 , in which the machine learning module includes a convolutional neural network. 
     
     
         28 . The system of  claim 21 , in which the machine learning module includes a neural network and the image portion is a block of pixels from the stitched image that includes pixels on both sides of the stitching boundary and all pixel values from the block of pixels are input to a first layer of the neural network. 
     
     
         29 . A method comprising:
 receiving a first image from a first image sensor;   receiving a second image from a second image sensor;   stitching the first image and the second image to obtain a stitched image;   identifying an image portion of the stitched image that is positioned on a stitching boundary of the stitched image;   inputting the image portion to a machine learning module to obtain a score, wherein the machine learning module has been trained using training data that included image portions labeled to reflect an absence of stitching and image portions labeled to reflect a presence of stitching, wherein the image portions labeled to reflect a presence of stitching included stitching boundaries of stitched images; and   storing, displaying, or transmitting the score or a composite score based in part on the score.   
     
     
         30 . The method of  claim 29 , comprising:
 identifying one or more additional image portions within the stitched image that occur along the stitching boundary of the stitched image;   inputting the one or more additional image portions to the machine learning module to obtain one or more additional scores; and   generating a histogram of the score and the one or more additional scores.   
     
     
         31 . The method of  claim 29 , comprising:
 training the machine learning module, wherein the training data includes image portions detected with a single image sensor that are labeled to reflect an absence of stitching.   
     
     
         32 . The method of  claim 29 , comprising:
 training the machine learning module, wherein the training data includes image portions labeled with subjective scores provided by humans for images from which the image portions are taken.   
     
     
         33 . The method of  claim 29 , comprising:
 selecting a parameter of a stitching algorithm based on the score.   
     
     
         34 . The method of  claim 29 , in which the image portion is a block of pixels from the stitched image that includes pixels on both sides of the stitching boundary, where the block of pixels has a resolution less than the resolution of the first image. 
     
     
         35 . The method of  claim 29 , in which the machine learning module includes a neural network that receives pixel values from pixels in the image portion and outputs the score. 
     
     
         36 . The method of  claim 29 , comprising:
 obtaining a plurality of scores from the machine learning module for a plurality of image portions from along the stitching boundary of the stitched image; and   determining a composite score for the stitched image based on the plurality of scores.   
     
     
         37 . A method comprising:
 labeling image portions in training data that were detected with a single image sensor to reflect the absence of stitching;   labeling image portions in training data that include a stitching boundary to reflect the presence of stitching; and   training a machine learning module using the labeled training data, wherein the machine learning module takes an image portion as input and outputs a score.   
     
     
         38 . The method of  claim 37 , in which the machine learning module includes a neural network and each labeled image portion in the training data is a block of pixels and all pixel values from the block of pixels are input to a first layer of the neural network. 
     
     
         39 . The method of  claim 37 , in which the machine learning module includes a convolutional neural network. 
     
     
         40 . The method of  claim 37 , in which the machine learning module includes a feature extraction submodule and a support vector machine.