Abstract: Systems and methods are disclosed for automated video quality assessment using a hybrid neural network architecture. A sequence of video frames is received and partitioned into fragments, which are further subdivided into patches and encoded as tokens. The tokens are processed in parallel by a state space model, configured to extract temporal features, and by a convolutional neural network, configured to extract spatial features. The resulting feature representations are combined to form a unified embedding, which is input to a prediction head to generate local and overall quality scores indicative of the perceptual quality of the video. In some embodiments, frame-level supervision is employed during training by comparing predicted per-frame scores to reference scores, improving accuracy and granularity.