Abstract: A system and method for confidence aided upsampling of categorical maps. In some embodiments, the method includes: determining a category of a first pixel of an image, the first pixel having a plurality of neighboring pixels, each of the neighboring pixels having a category; and processing the image based on the determined category. The determining may include: calculating a confidence weighted metric for each of the neighboring pixels, the confidence weighted metric being based on a maximum confidence value among each of the neighboring pixels; and determining the category of the first pixel based on the confidence weighted metric of each of the neighboring pixels and based on the category of one of the neighboring pixels.

Abstract: A method for computing a dominant class of a scene includes: receiving an input image of a scene; generating a segmentation map of the input image, the segmentation map being labeled with a plurality of corresponding classes of a plurality of classes; computing a plurality of area ratios based on the segmentation map, each of the area ratios corresponding to a different class of the plurality of classes of the segmentation map; and outputting a detected dominant class of the scene based on a plurality of ranked labels based on the area ratios.

Abstract: A method for computing a dominant class of a scene includes: receiving an input image of a scene; generating a segmentation map of the input image, the segmentation map being labeled with a plurality of corresponding classes of a plurality of classes; computing a plurality of area ratios based on the segmentation map, each of the area ratios corresponding to a different class of the plurality of classes of the segmentation map; and outputting a detected dominant class of the scene based on a plurality of ranked labels based on the area ratios.

Abstract: A method for computing a dominant class of a scene includes: receiving an input image of a scene; generating a segmentation map of the input image, the segmentation map being labeled with a plurality of corresponding classes of a plurality of classes; computing a plurality of area ratios based on the segmentation map, each of the area ratios corresponding to a different class of the plurality of classes of the segmentation map; and outputting a detected dominant class of the scene based on a plurality of ranked labels based on the area ratios.

Abstract: A method for computing a dominant class of a scene includes: receiving an input image of a scene; generating a segmentation map of the input image, the segmentation map being labeled with a plurality of corresponding classes of a plurality of classes; computing a plurality of area ratios based on the segmentation map, each of the area ratios corresponding to a different class of the plurality of classes of the segmentation map; and outputting a detected dominant class of the scene based on a plurality of ranked labels based on the area ratios.

Abstract: Provided are an image processing circuit, a system-on-chip including the same, and a method of improving quality of a first image. The image processing circuit includes a tuning circuit configured to receive a segmentation map including pixel-by-pixel class inference information of the first image and a confidence map including confidence of the class inference information, determine classes of respective pixels of the first image, correction effects for each pixel of the image, and correction values indicating intensity of the correction effects based on the segmentation map and the confidence map, and generate a correction map based on the classes and the correction values of the respective pixels; and at least one correcting circuit configured to generate an enhanced image by applying correction effects according to the correction values to the respective pixels of the first image based on the correction map.

Abstract: A system and method for confidence aided upsampling of categorical maps. In some embodiments, the method includes: determining a category of a first pixel of an image, the first pixel having a plurality of neighboring pixels, each of the neighboring pixels having a category; and processing the image based on the determined category. The determining may include: calculating a confidence weighted metric for each of the neighboring pixels, the confidence weighted metric being based on a maximum confidence value among each of the neighboring pixels; and determining the category of the first pixel based on the confidence weighted metric of each of the neighboring pixels and based on the category of one of the neighboring pixels.

Abstract: A method for computing a dominant class of a scene includes: receiving an input image of a scene; generating a segmentation map of the input image, the segmentation map being labeled with a plurality of corresponding classes of a plurality of classes; computing a plurality of area ratios based on the segmentation map, each of the area ratios corresponding to a different class of the plurality of classes of the segmentation map; and outputting a detected dominant class of the scene based on a plurality of ranked labels based on the area ratios.

Abstract: A method for computing a dominant class of a scene includes: receiving an input image of a scene; generating a segmentation map of the input image, the segmentation map including a plurality of pixels, each of the pixels being labeled with a corresponding class of a plurality of classes; computing a plurality of area ratios based on the segmentation map, each of the area ratios corresponding to a different class of the plurality of classes of the segmentation map; applying inference to generate a plurality of ranked labels based on the area ratios; and outputting a detected dominant class of the scene based on the plurality of ranked labels.

Abstract: A method for computing a dominant class of a scene includes: receiving an input image of a scene; generating a segmentation map of the input image, the segmentation map including a plurality of pixels, each of the pixels being labeled with a corresponding class of a plurality of classes; computing a plurality of area ratios based on the segmentation map, each of the area ratios corresponding to a different class of the plurality of classes of the segmentation map; applying inference to generate a plurality of ranked labels based on the area ratios; and outputting a detected dominant class of the scene based on the plurality of ranked labels.