A vehicle camera module may include: an image sensor; and an image transmission processor that may determine whether to compress or not compress an image captured through the image sensor according to the format of the image or a preset option, and output the compressed or non-compressed image to an external image output device. The image sensor and the image transmission processor may be implemented as an integrated module.

Systems and methods for encoding high resolution data associated with a relatively large number of bits to an encoded form having a relatively reduced number of bits. The method includes, by a processor: receiving an input image comprising one or more high resolution objects. The method further includes, for each of the one or more high resolution objects: identifying an object family for that object and determining whether a reference table exists for the object family. If a reference table exists for the object family, the method includes determining a size of that object, and identifying a tag based on the size. The method also includes encoding that object to form an encoded object having a relatively reduced number of bits, associating the identified tag with the encoded object, and saving the encoded object.

An image processing apparatus including a processor that performs: compressing target data for printing; decompressing the compressed target data; and when the resolution of the uncompressed target data is higher than the resolution of low-resolution data included in the compressed target data, generating sample data by decompressing only the low-resolution data, the sample data to be used for a process of detecting data of a particular image from the target data.

In a color to grayscale image conversion particularly method suitable for processing color document images such as forms, the color image is analyzed to determined which of the red, green and blue channels are the most dominant, second most dominant, and least dominant channels, based on the amount of information contained in each channel. Then, coefficients are assigned to the three channels, where the coefficient for the most dominant channel is smaller than the coefficient for the second most dominant color channel, which is in turn smaller than the coefficient for the least dominant color channel. The grayscale pixel value is then calculated using a linear combination of the red, green and blue pixel values weighted by their assigned coefficients. In one example, the ratio of the coefficients for the least dominant, the second most dominant and the most dominant channels is 10:3:1.

An image processing method includes an image encoding step of encoding data in which one channel in each pixel of image data including a plurality of pixels is configured by 2 bits or more into data in which the one channel is configured by 1 bit, a transfer step of transferring the encoded data to a printing apparatus, and an image decoding step of decoding the encoded data using the printing apparatus, in which, in the image encoding step, flag information indicating whether the pixel is a character or a line image or a photograph is assigned, and in the image decoding step, decoding is performed by selecting a first decoding method or a second decoding method based on the flag information.

An image processing method includes an image encoding step of encoding data in which one channel in each pixel of image data including a plurality of pixels is configured by 2 bits or more into data in which the one channel is configured by 1 bit, a transfer step of transferring the encoded data to a printing apparatus, and an image decoding step of decoding the encoded data using the printing apparatus, in which, in the image encoding step, flag information indicating whether the pixel is a character or a line image or a photograph is assigned, and in the image decoding step, decoding is performed by selecting a first decoding method or a second decoding method based on the flag information.

A learning model generating device includes a first image reading device and a first control device. The first control device includes a processor and functions, through the processor executing a first control program, as a first segmenter, a learning model generator, and a first compressor. The first segmenter segments each of images of training prints obtained by reading performed by the first image reading device. The learning model generator learns segmented images to generate a first learning model for use in inferring a type of an image defect. The first compressor compresses each of the images of the training prints. The first segmenter segments each of compressed images obtained by compression performed by the first compressor. The learning model generator learns compressed and segmented images to generate a second learning model for use in inferring a type of an image defect.

Examples of systems and methods for enhancing image quality for documents with highlighted portions are described. A highlighted portion from a received input is detected. The highlighted portion is automatically segmented as a text layer and the remaining portion is further segmented as a separate text layer and an image layer. A resolution is assigned to the highlighted portion, wherein the resolution assigned to the highlighted portion is greater than the respective resolution for the text layer and the image layer, to improve quality of the highlighted portion. The text layer corresponding to the highlighted portion and the separate text layer and the image layer are integrated together to generate a scanned document in a Mixed Raster Content (MRC) file format.

Examples of systems and methods for enhancing image quality for documents with highlighted portions are described. A highlighted portion from a received input is detected. The highlighted portion is automatically segmented as a text layer and the remaining portion is further segmented as a separate text layer and an image layer. A resolution is assigned to the highlighted portion, wherein the resolution assigned to the highlighted portion is greater than the respective resolution for the text layer and the image layer, to improve quality of the highlighted portion. The text layer corresponding to the highlighted portion and the separate text layer and the image layer are integrated together to generate a scanned document in a Mixed Raster Content (MRC) file format.

Examples of systems and methods for enhancing image quality for documents with highlighted portions are described. A highlighted portion from a received input is detected. The highlighted portion is automatically segmented as a text layer and the remaining portion is further segmented as a separate text layer and an image layer. A resolution is assigned to the highlighted portion, wherein the resolution assigned to the highlighted portion is greater than the respective resolution for the text layer and the image layer, to improve quality of the highlighted portion. The text layer corresponding to the highlighted portion and the separate text layer and the image layer are integrated together to generate a scanned document in a Mixed Raster Content (MRC) file format.