In some examples, a supply component for a printing device includes a memory device comprising a plurality of corrective data to produce a corresponding plurality of color tables each customized for a respective media type of a plurality of media types, wherein a respective corrective data of the plurality of corrective data corresponds to nodes of a reference color table for the printing device that is able to accept the plurality of media types for printing. The respective corrective data includes a plurality of residual values to transform the nodes of the reference color table to a customized color table for use with a selected media type of the plurality of media types.

In some examples, a print cartridge includes a memory device comprising quantized coefficients derived from a lossy compression, at a selected step size, of a difference color table including a plurality of difference nodes in which each difference node represents a difference value that is a difference of a value of a node of a color table and a value of a corresponding node of a reference table, the quantized coefficients useable to produce a reconstructed difference color table including a first set of difference nodes each representing a difference value that is within an error threshold at the selected step size, and a second set of difference nodes each representing a difference value that is outside an error threshold at the selected step size. The memory device further comprises corrective information to correct the second set of difference nodes of the reconstructed difference color table.

In some examples, a print cartridge includes a memory device comprising quantized coefficients derived from a compression of a difference table including a plurality of difference nodes in which each difference node represents a value that is a difference of a value of a node of a color table and a value of a corresponding node of a reference table, the quantized coefficients useable to produce a reconstructed difference table, and a plurality of residue nodes in which each residue node represents a value that is a difference of a value of a node of the color table and a value of a corresponding node of the reconstructed difference table.

Disclosed herein are methods, systems, and devices for solving the problem of caching large medical images during workflow. In one embodiment, a method is implemented on at least one computing device. The method includes receiving a source medical image file from a first remote device; caching the source medical image file in local memory; determining relevant medical image data, first non-relevant medical image data, and second non-relevant medical image data within the source medical image file; removing the second non-relevant medical image data to create a memory reduced medical image file; storing the memory reduced medical image file in the local memory; and transmitting the memory reduced medical image file to a second remote device.

An image compression method includes: starting a social media application to enter an interface for inputting a to-be-posted content; when an operation for adding an image is detected, displaying a first image in the interface; and when an operation for posting the first image is detected, in response to the operation, performing differential compression on the first image to obtain a second image, and sending the second image to a server corresponding to the social application. The method enables an electronic device to perform differential compression on the image and post the compressed image on a social media platform.

Disclosed herein are methods, systems, and devices for solving the problem of caching large medical images during workflow. In one embodiment, a method is implemented on at least one computing device. The method includes receiving a source medical image file from a first remote device; caching the source medical image file in local memory; determining relevant medical image data, first non-relevant medical image data, and second non-relevant medical image data within the source medical image file; removing the second non-relevant medical image data to create a memory reduced medical image file; storing the memory reduced medical image file in the local memory; and transmitting the memory reduced medical image file to a second remote device.

The present specification relates to image capture. More specifically, it relates to selective image capture for sensor carrying devices or floats deployed, for example, on the open sea. In one form, data is generated on the sensor carrying devices or floats by an on-board Inertial Measurement Unit (IMU) and is used to automatically predict the wave motion of the sea. These predictions are then used to determine an acceptable set of motion parameters that are used to trigger the on-board camera(s). The camera(s) then capture images. One consideration is that images captured at or near the peak of a wave crest with minimal pitch and roll will contain fewer obstructions (such as other waves). Such images provide a view further into the horizon to, for example, monitor maritime sea traffic and other phenomenon. Therefore, the likelihood of capturing interesting objects such as ships, boats, garbage, birds, . . . etc. is increased. These images may then be further processed and/or transmitted in a variety of manners.

An example embodiment may involve obtaining an input pixel map of a digital image containing an array of a×b pixel macro-cells; classifying each of the a×b pixel macro-cells as P class cells for substantially lossless compression or Q class cells for lossy compression; creating a first intermediate pixel map representing: the P class cells as is, and the Q class cells with all zero values; creating a second intermediate pixel map representing: the P class cells with all zero values, and the Q class cells as is; encoding the first intermediate pixel map into a first output stream by using substantially lossless compression; and encoding the second intermediate pixel map into a second output stream by: downsampling the P class cells and the Q class cells therein, and serializing representations the downsampled cells.

An example embodiment may involve obtaining an input pixel map of a digital image containing an array of a×b pixel macro-cells; classifying each of the a×b pixel macro-cells as P class cells for substantially lossless compression or Q class cells for lossy compression; creating a first intermediate pixel map representing: the P class cells as is, and the Q class cells with all zero values; creating a second intermediate pixel map representing: the P class cells with all zero values, and the Q class cells as is; encoding the first intermediate pixel map into a first output stream by using substantially lossless compression; and encoding the second intermediate pixel map into a second output stream by: downsampling the P class cells and the Q class cells therein, and serializing representations the downsampled cells.

In one aspect, a method of fast data compression operates on input data comprising plural J-bit bytes (e.g., 16-bit bytes). The method computes a first difference value between one pair of the input J-bit bytes, and determines that this first difference value can be represented by K bits, where K<J. The method further computes a second difference value between a second pair of the input J-bit bytes, and determines that this second difference value can be represented by M bits, where M<K. These K- and M-bit difference values are included in a composite output data string that also includes four data tags. One tag indicates the first difference value is represented by K bits. Another indicates the second difference value is represented by M bits. The final two tags indicate the polarities of the first and second difference values. A great variety of other features and arrangements are also detailed.