Real-time micro air-quality indexing
A first image may be received by a processor. The processor may identify an image quality measure of which to evaluate the first image. The processor may compare the first image to one or more images. The processor may generate a first image quality score for the first image based on the comparing. The processor may convert the first image quality score into a first micro air-quality index. The processor may transmit the first micro air-quality index to a recording device. Additionally, a recording device may capture a first image. The recording device may send the first image to a database that may include a model associated with an image quality measure. The recording device may receive a first micro air-quality index associated with the image quality measure. The recording device may rearrange a display of the first image to display the first micro air-quality index.
1. A computer-implemented method comprising:
receiving, from a recording device, a first image, wherein the first image includes a primary feature;
identifying an image quality measure of which to evaluate the first image;
comparing, based on the image quality measure, the first image to one or more images within a repository, wherein the one or more images include the same primary feature of the first image;
generate a first image quality score for the first image based on the comparing of the first image to the one or more images;
converting the first image quality score into a first micro air-quality index for the first image; and
transmitting the first micro air-quality index for the first image to the recording device.
2. The method of claim 1 , wherein the first image includes an origination tag, and wherein comparing the first image to the one or more images within the repository further comprises:
identifying a model associated with the image quality measure;
identifying that the one or more images are associated with metadata corresponding to the origination tag;
identifying that each of the one or more images include the primary feature of the first image;
segmenting each of the one or more images into one or more portions based on the image quality measure; and
assigning, based on the segmenting, each of the one or more images a respective image quality score.
3. The method of claim 2 , wherein segmenting each of the one or more images further comprises:
determining that one or more secondary features are above an image quality measure threshold; and
identifying a respective amount of each of the one or more secondary features included in each of the one or more images.
4. The method of claim 3 , further comprising:
identifying an amount of one or more secondary features included in the first image;
segmenting the first image into one or more portions; and
identifying which respective amount of the one or more secondary features of the one or more images corresponds to the amount of the one or more secondary features included in the first image.
5. The method of claim 4 , further comprising:
identifying that the image quality measure is sharpness of the one or more images and the first image;
training the model to correlate the image quality scores, based on sharpness, of the one or more images and the first image to air quality; and
determining, from the image quality scores of the one or more images, a micro air-quality index for each of the one or more images.
6. The method of claim 1 , wherein noise of the first image is filtered out of the first image prior to identifying the image quality measure.
7. The method of claim 6 , wherein the noise of the first image is filtered out of the first image by normalizing the first image with the one or more images within the repository.