Adobe, Inc.

Other

Jon D Clauson

Douglas Charles Shimonek

Douglas E Meisner

David J Howe

Lance Lewis

Bryan O'Neil Hughes

Christopher P Hondl

Techniques are disclosed for facilitating the sharing of digital imaging effects from an image originator to an image recipient. A digital imaging effect is the result of a processing technique that is applied to a digital image in a way that produces a particular visual effect. The processing techniques that underlie digital imaging effects such as these can be defined by parameters that establish how the individual pixels comprising the image are manipulated to achieve the desired visual effect. In certain embodiments such defining parameters can be incorporated into a digital image file that thus contains not only data defining the image itself, but also parameters that define one or more effects that may have been applied to the image. This advantageously allows a recipient of the digital image file to not only view the image, but also to use the incorporated parameters to apply the effect to other images.

Image effect extraction

A method and apparatus for preparing an image for downloading over a link. The method includes receiving a user selection for an image to prepare, retrieving current user settings reflective of desired settings for compressing the image and automatically presenting a plurality of variations of the image to the user where each variation is derived using compression settings that are scaled from the current user settings.

Automatically preparing an image for download

Methods and apparatus for generating a compressed image from a source image by compressing the source image using a dictionary based lossy compression algorithm that regionally varies the amount of information loss from the source image based on regional image quality levels contained in an image quality mask. The image quality mask can be an alpha channel of the source image, can be stored as a raster map, or can be stored as a resolution independent function. The regional image quality levels in the image quality mask can be determined by a user, automatically generated from the image, or automatically generated from user input. The dictionary based lossy compression algorithm can be a lossy Lempel-Ziv-Welch (LZW) compression algorithm.

Variable lossy compression

Methods and apparatus, including computer program products, are used to compress a true color image in such a way that the compressed image may be decompressed by a decompression method according to a dictionary-based compression technique. A color table defines a mapping from true colors to index color values. A set of zero or more candidate strings for a current pixel in the image is identified in a compression dictionary. Each candidate string corresponds to a string of pixels in the image, with the last pixel of the string corresponding to the current pixel. Each candidate string approximately matches the corresponding image pixel string. If the set of candidate strings for the current pixel is empty, one of the candidate strings for the previous current pixel is selected, and a code for the selected string is added to a compressed representation of the image.

Data compression system and technique

A method for generating a palette-based output image from a source image by variably dithering regions of the source image using an associated regional dithering mask. The palletized output image can be a GIF image or a PNG8 image. The dithering mask contains a plurality of dithering levels specifying the degree to which colors in corresponding regions of the source image can be dithered to paint the output image. The dithering mask stores dithering levels for the source image on a per pixel basis. The dithering mask is stored as an alpha channel of the source image. The palletized output file is generated from the source image on a per pixel basis. For each pixel, a target color is calculated by adding an accumulated error from neighboring pixels to the true color of a corresponding pixel in the source image. The output pixel is painted with the closest color to the target color that is available in the output file's color table. A color error is calculated by subtracting the output pixel's paint color from it's target color, and multiplying the difference by a dithering level obtained from a corresponding pixel in a dithering mask. The color error is diffused to neighboring pixels according to a dithering algorithm, such as the Floyd-Steinberg dithering algorithm.

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Chanhassen, MN, United States of America

Jon D Clauson