Image file formats

About: Image file formats is a research topic. Over the lifetime, 10349 publications have been published within this topic receiving 102407 citations.

Apparatus and method for processing image and computer readable recording medium with image-processing program recorded

Abstract: PROBLEM TO BE SOLVED: To improve a convenience when cumulative image data is to be deleted. SOLUTION: A CPU 10 registers image types of image files stored in a storage device 14 to an image management table for each of printer images to be printed by a printer 15, fax images to be received and transmitted by a FAX 16, copy images to be copied by the printer 15, and scanner images inputted by a scanner 17, provides a priority order for each image type to be deleted, inspects an idle capacity of the storage device 14 when an image file is to be stored, deletes image files based on the priority order from the storage device 14 when judging based on the inspection that a space for storing the image file is short of capacity, and forms a region of a capacity by which image data can be stored.

Systems and methods for barcode integration in packaging design and printing

Abstract: Methods and systems for barcode integration in packaging design and printing are described. The method includes receiving a request for a For Position Only (FPO) image file which is a placeholder for a final barcode. The method includes accessing barcode data, wherein the barcode data contains information usable to produce the final barcode, generating the FPO image file including an FPO image and including metadata containing the barcode data, and sending the FPO image file including the metadata to a second computer device. A final barcode can be generated from the metadata and by accessing at least one printing parameter of a prepress system, and the final barcode can be used to replace the FPO image file.

Image file arrangement for use with an improved image quality assurance system

Abstract: An image file arrangement for identifying image defects in a plurality of imaged documents, where each imaged document includes at least one image rendition. The image file arrangement comprises an image quality flag file having a record entry for each of the plurality of imaged documents that has at least one image rendition with at least one identified image defect, wherein each record entry includes information about the at least one identified image condition for the least one flagged image rendition and an image index file including an image index file record for each imaged document, wherein for each imaged document having at least one image rendition having at least one identified image defect, the image index file record for that document includes a reference to the corresponding image quality flag file record and the image quality flag file record includes a reference to the corresponding image index file record.

Searching For Hidden Messages: Automatic Detection of Steganography

Abstract: Steganography is the field of hiding messages in apparently innocuous media (e.g. images), and steganalysis is the field of detecting these covert messages. Almost all steganalysis consists of hand-crafted tests or human visual inspection to detect whether a file contains a message hidden by a specific steganography algorithm. These approaches are very fragile ‐ trivial changes in a steganography algorithm will often render a steganalysis approach useless, and human inspection does not scale. We propose a machine learning (ML) approach to steganalysis. First, a media file is represented as a canvas ‐ the available space within the file to hide a message. Those features that can distinguish clean from stegobearing files are then selected. We use ML algorithms to distinguish clean and stego-bearing files. The results reported here show that ML algorithms work in both content- and compression-based image formats, outperforming at least one current hand crafted steganalysis technique in the latter. Our current work can detect previously seen (trained on) steganography techniques, and we discuss extensions that we believe will be able to detect steganography using more sophisticated algorithms, as well as the use of previously unseen steganography algorithms.

An image database for benchmarking of automatic face detection and recognition algorithms

Abstract: This paper describes a face image database which has been created and developed at Kodak as a common database for direct benchmarking of automatic face detection and recognition algorithms. This consumer application-oriented face image database is composed of two main sub-databases, one for face detection, and one for face recognition. The database is intended to be distributed to researchers both inside and outside of Kodak working in face detection and recognition research. The database contains pictures taken using consumer digital cameras, scanned in from a photo scanner, as well as pictures from Kodak Image Magic picture disks. The details of the face database such as the development process and the image file formats, are described, together with a discussion on some application scenarios, as well as current benchmarking activities.