Neural network solution for a real-time no-reference video quality assessment of H.264/AVC video bitstreams

The ever-growing video streaming services require accurate quality assessment with often no reference to the original media. One primary challenge in developing no-reference (NR) video quality metrics is achieving real-timeliness while retaining the accuracy. A real-time no-reference video quality assessment (VQA) method is proposed for videos encoded by H.264/AVC codec. Temporal and spatial features are extracted from the encoded bit-stream and pixel values to train and validate a fully connected neural network. The hand-crafted features and network dynamics are designed in a manner to ensure a high correlation with human judgment of quality as well as minimizing the computational complexities. Proof-of-concept experiments are conducted via comparison with: 1) video sequences rated by a full-reference quality metric, and 2) H.264-encoded sequences from the LIVE video dataset which are subjectively evaluated through differential mean opinion scores (DMOS). The performance of the proposed method is verified by correlation measurements with the aforementioned objective and subjective scores. The framework achieves real-time execution while outperforming state-of-art full-reference and no-reference video quality assessment methods.