Light Reference Video Quality Estimation with Embedding of Compact Features
One key issue in the content delivery network service is efficient and effective video quality assessment. Yet the legacy method obtaining the results via detailed calculation based on the original sequence or subjective experiments which lacks efficiency. In this work, we developed a compact feature based solution that allows for on-demand quality assessment with limited light references. Compact feature subspace embedding and SVM classifiers are trained to derive Quality of Experience (QoE) labels from the combination of the original and corresponding reconstructed compact features. Overhead in computation complexity and communication cost is minimum. Simulation results demonstrated the robustness and universality of the proposed solution.