A method comprising: determining, using one or more processors, an image quality score associated with an image, including: determining a foreground and a background in the image; calculating a set of one or more characteristic parameters of the image based on the foreground and the background, wherein the one or more characteristic parameters comprises a foreground-background contrast, and wherein the foreground-background contrast is determined by at least: determining foreground color zones of the foreground and calculating an area ratio between an area of a foreground color zone and an area of the foreground, wherein the determining of the foreground color zones includes clustering pixels in the foreground to obtain a first number of color types; determining background color zones and calculating an area ratio between an area of a background color zone and the area of the foreground, wherein the determining of the background color zones includes clustering pixels in the background to obtain a second number of color types; calculating a Euclidean color-based distance between the foreground color zone and the background color zone; and determining the foreground-background contrast based on the Euclidean color-based distance and the area ratios; and calculating the image quality score based at least in part on the set of characteristic parameters, wherein calculating the image quality score comprises using an image quality computation model that has been pre-trained; and in response to a search query, generating a set of search results that includes a set of images, wherein inclusion of the images or ranking of the search results is based at least in part on image quality scores associated with the set of images. determining, using one or more processors, an image quality score associated with an image, including: determining a foreground and a background in the image; calculating a set of one or more characteristic parameters of the image based on the foreground and the background, wherein the one or more characteristic parameters comprises a foreground-background contrast, and wherein the foreground-background contrast is determined by at least: determining foreground color zones of the foreground and calculating an area ratio between an area of a foreground color zone and an area of the foreground, wherein the determining of the foreground color zones includes clustering pixels in the foreground to obtain a first number of color types; determining background color zones and calculating an area ratio between an area of a background color zone and the area of the foreground, wherein the determining of the background color zones includes clustering pixels in the background to obtain a second number of color types; calculating a Euclidean color-based distance between the foreground color zone and the background color zone; and determining the foreground-background contrast based on the Euclidean color-based distance and the area ratios; and calculating the image quality score based at least in part on the set of characteristic parameters, wherein calculating the image quality score comprises using an image quality computation model that has been pre-trained; and determining a foreground and a background in the image; calculating a set of one or more characteristic parameters of the image based on the foreground and the background, wherein the one or more characteristic parameters comprises a foreground-background contrast, and wherein the foreground-background contrast is determined by at least: determining foreground color zones of the foreground and calculating an area ratio between an area of a foreground color zone and an area of the foreground, wherein the determining of the foreground color zones includes clustering pixels in the foreground to obtain a first number of color types; determining background color zones and calculating an area ratio between an area of a background color zone and the area of the foreground, wherein the determining of the background color zones includes clustering pixels in the background to obtain a second number of color types; calculating a Euclidean color-based distance between the foreground color zone and the background color zone; and determining the foreground-background contrast based on the Euclidean color-based distance and the area ratios; and determining foreground color zones of the foreground and calculating an area ratio between an area of a foreground color zone and an area of the foreground, wherein the determining of the foreground color zones includes clustering pixels in the foreground to obtain a first number of color types; determining background color zones and calculating an area ratio between an area of a background color zone and the area of the foreground, wherein the determining of the background color zones includes clustering pixels in the background to obtain a second number of color types; calculating a Euclidean color-based distance between the foreground color zone and the background color zone; and determining the foreground-background contrast based on the Euclidean color-based distance and the area ratios; and calculating the image quality score based at least in part on the set of characteristic parameters, wherein calculating the image quality score comprises using an image quality computation model that has been pre-trained; and in response to a search query, generating a set of search results that includes a set of images, wherein inclusion of the images or ranking of the search results is based at least in part on image quality scores associated with the set of images.