Abstract: For each of a number of regions of interest (ROI) types, ROIs are extracted from a reference image based on an object map distinguishing symbol, raster, and vector objects within the reference image. Whether print quality of a printing device has degraded below a specified acceptable print quality level is assessed based on a comparison of the extracted ROIs within the reference image to corresponding ROIs within a test image corresponding to the reference image and printed by the printing device.

Abstract: For each region of interest (ROI) type of a number of ROI types, ROIs of the ROI type within a reference image are compared to corresponding ROIs within a test image corresponding to the reference image and printed by a printing device. For each ROI type, a feature vector characterizing image quality defects within the test image for the ROI type is generated based on results of the comparing for the ROI type. Whether print quality of the printing device has degraded below a specified acceptable print quality level can be assessed based on the feature vectors for the ROI types.

Abstract: An example of an apparatus including a communication interface to receive an image file is provided. The image file represents a scanned image of a output generated by a printing device. The apparatus further includes an identification engine to process the image file with a convolutional neural network model to identify a feature. The feature may be indicative of a potential failure. The apparatus also includes an image analysis engine to indicate a life expectancy of a part associated with the potential failure based on the feature. The image analysis engine uses the convolutional neural network model to determine life expectancy.

Abstract: In an example method, an image of a printed document is descreened. The descreened image is separated into blocks. A smooth area of the descreened image is selected based on an averaging of localized color distance values of pixels in each of the blocks. A streak in a block of the smooth area is detected in response to detecting that a magnitude of a color distance projection of the block exceeds a threshold value.

Abstract: An example of an apparatus including a communication interface to receive an image file is provided. The image file represents a scanned image of a output generated by a printing device. The apparatus further includes an identification engine to process the image file with a convolutional neural network model to identify a feature. The feature may be indicative of a potential failure. The apparatus also includes an image analysis engine to indicate a life expectancy of a part associated with the potential failure based on the feature. The image analysis engine uses the convolutional neural network model to determine life expectancy.

Abstract: In an example method, an image of a printed document is descreened. The descreened image is separated into blocks. A smooth area of the descreened image is selected based on an averaging of localized color distance values of pixels in each of the blocks. A streak in a block of the smooth area is detected in response to detecting that a magnitude of a color distance projection of the block exceeds a threshold value.

Abstract: Pixel blocks of a user-generated print job raster page are clustered into raster color clusters, and pixel blocks of a corresponding scanned page are clustered into scanned color clusters. For each raster color cluster, if a color difference between the cluster and a corresponding scanned color cluster is greater than a threshold, color fading of the cluster is determined from simulated depletion of each colorant of a printing device, and a likely depleted colorant is determined based on the corresponding scanned color cluster and the determined color fading. A most likely depleted colorant is determined from the likely depleted colorant predicted for each raster color cluster for which the determined color difference is greater than the threshold.

Abstract: Pixel blocks of a user-generated print job raster page are clustered into raster color clusters, and pixel blocks of a corresponding scanned page are clustered into scanned color clusters. For each raster color cluster, if a color difference between the cluster and a corresponding scanned color cluster is greater than a threshold, color fading of the cluster is determined from simulated depletion of each colorant of a printing device, and a likely depleted colorant is determined based on the corresponding scanned color cluster and the determined color fading. A most likely depleted colorant is determined from the likely depleted colorant predicted for each raster color cluster for which the determined color difference is greater than the threshold.

Abstract: In some examples, print quality diagnosis may include aligning corresponding characters between a scanned image of a printed physical medium aligned to a master image associated with generation of the printed physical medium to generate a common mask. Print quality diagnosis may further include determining, for each character of the scanned image, an average value associated with pixels within the common mask, and determining, for each corresponding character of the master image, the average value associated with pixels within the common mask. Further, print quality diagnosis may include determining, for each character of the common mask, a metric between the average values associated with the corresponding characters in the scanned and master images.

Abstract: In some examples, print quality diagnosis may include aligning corresponding characters between a scanned image of a printed physical medium aligned to a master image associated with generation of the printed physical medium to generate a common mask. Print quality diagnosis may further include determining, for each character of the scanned image, an average value associated with pixels within the common mask, and determining, for each corresponding character of the master image, the average value associated with pixels within the common mask. Further, print quality diagnosis may include determining, for each character of the common mask, a metric between the average values associated with the corresponding characters in the scanned and master images.