In an image data conversion device, color image data is represented in gray scale, a histogram of brightness values is created for the gray-scaled image data, it is determined based on the created histogram which image pattern of a plurality of image patterns the gray-scaled image data is classified into, a range subjected to gamma correction and a range fixed to at least one of a minimum value and a maximum value of gray scale are set for each image pattern, and image data conversion including the gamma correction is performed on the gray-scaled image data.

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.

Index-based geospatial analysis may include applying a first and second index-based analysis for a set of imagery. The set of imagery may include a location-specific index values used to form a histogram for a single index image (e.g., for a single surveyed field). This first analysis may be referred to as an acre-to-acre mapping, which may be useful for identifying differences in indices (e.g., NDVI vegetative health) of different parts of the field from a single day. A second day-to-day index-based analysis may be performed by calculating a histogram for each set of imagery from multiple days, combining the histograms, and generating a single equal-area index map. The index map can be applied to redistribute the histogram values within multiple days of data, which may provide a more useful map of variation in each individual image and changes between images.

Embodiments of the present invention disclose a method, computer program product, and system for cataloging images based on a gridded color histogram analysis. The computer accesses an image gallery specified by a user, wherein the image gallery is at least one of an image gallery stored on a user computing device, an image gallery stored on a user account at a third-party image storage, or an image gallery searched on the web. The computer receives a request to search the image gallery specified by the user. The computer performs a search of the image gallery, wherein the search is using a color based histogram algorithm based on a user input. The computer transmits a cataloged and sorted image gallery to the user computing device to be displayed.

An image processing apparatus includes a target value calculation unit configured to calculate a target value to be output in a predetermined region in input image data based on pixel values of pixels included in the region, a distribution order determination unit configured to determine a distribution order of output values for distributing output values corresponding to the target value in the region based on a pixel value of each pixel included in the region and a threshold value in the threshold matrix corresponding to the pixel, and an output value determination unit configured to determine an output value of each pixel included in the region by allocating the target value to at least one pixel included in the region in the distribution order.

Embodiments of the present invention disclose a method, computer program product, and system for cataloging images based on a gridded color histogram analysis. The computer accesses an image gallery specified by a user, wherein the image gallery is at least one of an image gallery stored on a user computing device, an image gallery stored on a user account at a third-party image storage, or an image gallery searched on the web. The computer receives a request to search the image gallery. The computer performs a search of the image gallery, wherein the search is using a color based histogram algorithm based on a user input. The computer transmits a cataloged and sorted image gallery to the user computing device to be displayed.

In an image data conversion device, color image data is represented in gray scale, a histogram of brightness values is created for the gray-scaled image data, it is determined based on the created histogram which image pattern of a plurality of image patterns the gray-scaled image data is classified into, a range subjected to gamma correction and a range fixed to at least one of a minimum value and a maximum value of gray scale are set for each image pattern, and image data conversion including the gamma correction is performed on the gray-scaled image data.

A converter can process image data from input HDR images in real time to compute new metadata about the brightness, contrast, color gamut and/or color volume for the image data to be displayed from each frame. Existing metadata can be ignored. The converter can combine the metadata for a current HDR frame with metadata for a plurality of immediately previous sequential frames to provide parameters for tone mapping. The converter uses these parameters, and characteristics about a lower dynamic range display which will receive output image data, to define a transfer function for converting the input HDR image data into output image data for display. The converter analyzes and tone maps HDR frames at a rate sufficient to allow output video to be generated at a desired frame rate while receiving the image data from frames of the input HDR video at an input frame rate.

An image processing apparatus according to this embodiment performs, based on a job setting, conversion of a pixel value of a partial region in an input original image into a predetermined value on image data of the original image, as needed. Subsequently, this image processing apparatus generates a histogram representing the density signal distribution of the image data of the original image or the converted image data. Note that if the above-described conversion is performed, this image processing apparatus corrects the generated histogram by subtracting, from a count of the predetermined number of the generated histogram, the number of sampling points counted in the above-described partial region when the histogram is generated.

Embodiments related to local tone mapping for symbol reading. A local pixel neighborhood metric is determined for at least one raw pixel in a region-of-interest, which identifies on one or more raw pixels near the at least one raw pixel. A local mapping function is determined for the at least one raw pixel that maps the value of the raw pixel to a mapped pixel value with a mapped bit depth that is smaller than the bit depth associated with the raw image. The local mapping function is based on a value of at least one other raw pixel near the at least one raw pixel within the local pixel neighborhood metric, and at least one parameter determined based on the raw image. A mapped image is computed for the region-of-interest by applying the local mapping function to the raw image.