![]() ![]() In: 2019 IEEE international symposium on circuits and systems (ISCAS), pp 1–5 Google Scholar Fazliani Y, Andrade E, Shirani S (2019) Learning based hybrid no-reference video quality assessment of compressed videos. Fang R Al-Bayaty R Wu D BNB Method for no-reference image quality assessment IEEE Transactions on Circuits and Systems for Video Technology 2017 27 7 1381 1391 10.1109/TCSVT.2016.2539658 Google Scholar Digital Library In: Advances in neural information processing systems, pp 402–408 Google Scholar Caruana R, Lawrence S, Giles CL (2001) Overfitting in neural nets: backpropagation, conjugate gradient, and early stopping. Born RT Bradley DC Structure and function of visual area MT Ann Rev Neurosci 2005 28 1 157 189 10.1146/annurev.neuro.26.041002.131052 Google Scholar Cross Ref In: Asia-Pacific signal and information processing association annual summit and conference (APSIPA ASC), pp 1513–1517 Google Scholar Ahn S, Lee S (2018) No-reference video quality assessment based on convolutional neural network and human temporal behavior. Abadi M, Agarwal A, Barham P, Brevdo E, Chen Z, Citro C, Corrado GS, Davis A, Dean J, Devin M, Ghemawat S, Goodfellow I, Harp A, Irving G, Isard M, Jia Y, Jozefowicz R, Kaiser L, Kudlur M, Levenberg J, Mané D, Monga R, Moore S, Murray D, Olah C, Schuster M, Shlens J, Steiner B, Sutskever I, Talwar K, Tucker P, Vanhoucke V, Vasudevan V, Viégas F, Vinyals O, Warden P, Wattenberg M, Wicke M, Yu Y, Zheng X (2015) TensorFlow: large-scale machine learning on heterogeneous systems. The framework achieves real-time execution while outperforming state-of-art full-reference and no-reference video quality assessment methods. The performance of the proposed method is verified by correlation measurements with the aforementioned objective and subjective scores. 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 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. Temporal and spatial features are extracted from the encoded bit-stream and pixel values to train and validate a fully connected neural network. ![]() A real-time no-reference video quality assessment (VQA) method is proposed for videos encoded by H.264/AVC codec. One primary challenge in developing no-reference (NR) video quality metrics is achieving real-timeliness while retaining the accuracy. The ever-growing video streaming services require accurate quality assessment with often no reference to the original media. ![]()
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