Abstract: A video quality evaluation system comprises a training module, a calculation module, an analytical module, a designing module, an optimization module, and an estimation module. The training module collects training videos and trains labels of perceptual quality generation that is associated with the collected videos. The calculation module determines objective metrics based on the trained labels that are associated with the collected videos. The analytical module analyses scenes of the training video, and correlates objective metrics associated with the analysed scenes using perceptual parameters. The designing module designs a Convolutional Neural Network (CNN) Architecture based on data associated with the objective metrics and trains a model generated based on the designed CNN architecture. The optimization module optimizes the model and optimizes power after the model optimization. The estimation module estimates perceptual quality scores for incoming video data after the power optimization.

Abstract: A video quality evaluation system comprises a training module, a calculation module, an analytical module, a designing module, an optimization module, and an estimation module. The training module collects training videos and trains labels of perceptual quality generation that is associated with the collected videos. The calculation module determines objective metrics based on the trained labels that are associated with the collected videos. The analytical module analyses scenes of the training video, and correlates objective metrics associated with the analysed scenes using perceptual parameters. The designing module designs a Convolutional Neural Network (CNN) Architecture based on data associated with the objective metrics and trains a model generated based on the designed CNN architecture. The optimization module optimizes the model and optimizes power after the model optimization. The estimation module estimates perceptual quality scores for incoming video data after the power optimization.