Xerox Corporation

Apurba Das

Ranita Bej

Xing Li

Sankaralingam Madasamy

Prakash Kumarasamy

Sivakumar Muthaiah

Vijayabharathi Shetty

Methods, systems, and computer program products for improving the performance of a raster image processor. Smaller objects are identified among a group of larger objects with respect to a job processed via a raster image processor. The smaller objects are merged with one or more larger objects among the group of larger objects. The smaller objects that are merged with the larger object are treated as a single object without losing the perceptual quality of the job and while reducing memory requirements to thereby enhance productivity during processing of the job via the raster image processor. RIP performance improvement results by pre-flattening complicatedly designed backgrounds with multiple objects of significantly low relative occupancy.

Raster image processor methods and systems

Methods, systems, and computer-program products for optimizing SRE (Super Resolution Encoding) patterns. A hierarchical self-organizing pattern map (HSOPM) of SRE patterns can be derived, which illustrates interrelationships between consecutive SRE patterns. Such a hierarchical self-organizing map provides a first level of hierarchy, a second level of hierarchy, etc. Different weights can be assigned to different synthesis of traversal (SoT) according to the second level of hierarchy. The likelihood of the SRE patterns can then be calculated based on a fitness of continuity and the different weights, so as to subsequently select and encode an allowed number of the SRE patterns while replacing other patterns with a lower likelihood value with an immediate root and thereby adaptively optimize any number of the SRE patterns with respect to any number of values.

Adaptive optimization of super resolution encoding (SRE) patterns by hierarchical self-organized pattern map (HSOPM) and synthesis of traversal (SOT)

The disclosed embodiments illustrate methods and systems for encoding an image. The method includes identifying one or more objects, having an associated first tag value, in the image. The first tag value is deterministic of at least a type of the one or more objects. The number of the one or more objects in the image is less than a predetermined number of objects. The method further includes assigning a second tag value to each pixel in the image to create an encoded image. The second tag value is assigned based on the type of object represented by each pixel. The size of the second tag value is less than the size of the first tag value. The method further includes defining a header field for the encoded image. The header field includes the first tag value associated with each of the one or more objects.

Methods and systems for compressing electronic documents

This disclosure relates to a method and apparatus for implementing a trapping operation on a digital image during image processing and prorating the size of trap color filter with respect to local irregularity in shape of any target object. Some examples of the present disclosure calculate a plurality of prorated trapping parameters to be applied to portions of an object in a printing process, the calculation being based on repeated generation and application of a 2D Gaussian mask to a binarized object to identify disappeared portions of the object. The calculated plurality of prorated trapping parameters may be applied to the object during the printing process.

Calculation of trapping parameters

A method for varying a thickness of a trap around an object in an image. The method may include selecting a first window along a border of the object. The first window includes one or more first pixels representing the object and one or more second pixels representing a background or another object. A first edge orientation direction is determined based at least partially upon a location of the one or more first pixels in the first window. A first thickness of the object is measured along the first edge orientation direction. A trap is created around the object. A first thickness of the trap proximate to the first window is varied based at least partially upon the first thickness of the object.

Varying trap thickness in an image

The present disclosure relates to a computer-implemented method, device, and computer-readable storage medium used for trapping an object against a gradient background comprising: obtaining a trapping parameter for the object in both a fast scan and slow scan direction; forming a first, a second, and a third color trap for the object; comparing the first color trap for the object with the second color trap for the object; comparing the first color trap for the object with the third color trap for the object; determining that a result of the comparing the first color trap for the object with the second color trap for the object yields a larger result than a result of the comparing the first color trap for the object with the third color trap for the object; and applying trapping to the inner side of the object.

Method and system for forced unidirectional trapping ('FUT') for constant sweep color object-background combination

The present disclosure relates to a computer-implemented method, device, and computer-readable storage medium used to determine the prorating of trap color radius with respect to an object (text or graphics) size. The prorating of trap color radius allows for the problem of overpowering the trapping filter over the object size.

Method and system for prorating trapping parameters globally with respect to object size

A method on a computing device for categorizing one or more blocks of an image is disclosed. The method includes computing a membership value of each of the one or more blocks for each of one or more categories based on a set of parameters associated with each of the one or more blocks. The one or more categories comprise at least an image category. Each of the one or more blocks is categorized in the one or more categories based on the membership value. A category of at least one block is modified to the image category based on a reference signal and the membership value such that the number of blocks categorized under the image category increases.

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West Bengal, India

Apurba Das