Systems and methods for quality of experience computation

A system comprising:

• control circuitry configured to:
• train a first machine learning model to generate a neural feature for an inputted media frame using a loss function, wherein a first part of the loss function penalizes overall signal loss and a second part of the loss function penalizes texture loss, wherein the first machine learning model comprises weights;
• compute a first neural feature of a first media frame stored by a media server using the trained first machine learning model;
• cause the client device to receive the first neural feature;
• cause a client device to receive a second media frame as a part of a media stream from the media server, wherein the second media frame is a degraded version of the first media frame, wherein the client device is configured to:
• receive the weights of the first machine learning model;
• compute a second neural feature of the second media frame using a second trained machine learning model, wherein the second machine learning model is configured with the received weights of the first machine learning model;
• compute a Quality of Experience (QoE) metric based at least on the first neural feature and the second neural feature;
• and
• an input/output circuitry configured to:
• receive the QoE metric;
• and
• wherein the control circuitry is further configured to:
• modify, based on the QoE metric, at least one parameter of the media stream transmitted by the media server.