A device comprising: a user interface; and an image processing circuit, comprising: a memory; an attribute identification engine to identify an attribute from an input image based on a model stored in the memory; and a picture quality, in the following also referred to as PQ, engine to generate an output image for display by enhancing the input image based on the identified attribute; characterized in that the image processing circuit further comprises: a data collection module to generate a labeled image based on the input image labeled with the identified attribute, and to add the labeled image to a training database stored in the memory; and a training engine to re-train the model using the training database; wherein the data collection module is further operative to: receive, via the user interface, a user-identified attribute which changes the identified attribute for the input image; generate the labeled image based on the input image labeled with the user-identified attribute; retrieve a plurality of sample images from the training database, each sample image having a confidence level exceeding a predetermined threshold with respect to the user-identified attribute; and provide each sample image for the user to label to thereby generate labeled images for the training database. a user interface; and an image processing circuit, comprising: a memory; an attribute identification engine to identify an attribute from an input image based on a model stored in the memory; and a picture quality, in the following also referred to as PQ, engine to generate an output image for display by enhancing the input image based on the identified attribute; characterized in that the image processing circuit further comprises: a data collection module to generate a labeled image based on the input image labeled with the identified attribute, and to add the labeled image to a training database stored in the memory; and a training engine to re-train the model using the training database; wherein the data collection module is further operative to: receive, via the user interface, a user-identified attribute which changes the identified attribute for the input image; generate the labeled image based on the input image labeled with the user-identified attribute; retrieve a plurality of sample images from the training database, each sample image having a confidence level exceeding a predetermined threshold with respect to the user-identified attribute; and provide each sample image for the user to label to thereby generate labeled images for the training database. a memory; an attribute identification engine to identify an attribute from an input image based on a model stored in the memory; and a picture quality, in the following also referred to as PQ, engine to generate an output image for display by enhancing the input image based on the identified attribute; characterized in that the image processing circuit further comprises: a data collection module to generate a labeled image based on the input image labeled with the identified attribute, and to add the labeled image to a training database stored in the memory; and a training engine to re-train the model using the training database; wherein the data collection module is further operative to: receive, via the user interface, a user-identified attribute which changes the identified attribute for the input image; generate the labeled image based on the input image labeled with the user-identified attribute; retrieve a plurality of sample images from the training database, each sample image having a confidence level exceeding a predetermined threshold with respect to the user-identified attribute; and provide each sample image for the user to label to thereby generate labeled images for the training database. a data collection module to generate a labeled image based on the input image labeled with the identified attribute, and to add the labeled image to a training database stored in the memory; and a training engine to re-train the model using the training database; wherein the data collection module is further operative to: receive, via the user interface, a user-identified attribute which changes the identified attribute for the input image; generate the labeled image based on the input image labeled with the user-identified attribute; retrieve a plurality of sample images from the training database, each sample image having a confidence level exceeding a predetermined threshold with respect to the user-identified attribute; and provide each sample image for the user to label to thereby generate labeled images for the training database. receive, via the user interface, a user-identified attribute which changes the identified attribute for the input image; generate the labeled image based on the input image labeled with the user-identified attribute; retrieve a plurality of sample images from the training database, each sample image having a confidence level exceeding a predetermined threshold with respect to the user-identified attribute; and provide each sample image for the user to label to thereby generate labeled images for the training database.