Name
Title | ||
|---|---|---|
No-Reference Video Quality Assessment Based on Artifact Measurement and Statistical Analysis |
Abstract | ||
|---|---|---|
A discrete cosine transform (DCT)-based no-reference video quality prediction model is proposed that measures artifacts and analyzes the statistics of compressed natural videos. The model has two stages: 1) distortion measurement and 2) nonlinear mapping. In the first stage, an unsigned ac band, three frequency bands, and two orientation bands are generated from the DCT coefficients of each decoded frame in a video sequence. Six efficient frame-level features are then extracted to quantify the distortion of natural scenes. In the second stage, each frame-level feature of all frames is transformed to a corresponding video-level feature via a temporal pooling, then a trained multilayer neural network takes all video-level features as inputs and outputs, a score as the predicted quality of the video sequence. The proposed method was tested on videos with various compression types, content, and resolution in four databases. We compared our model with a linear model, a support-vector-regression-based model, a state-of-the-art training-based model, and a four popular full-reference metrics. Detailed experimental results demonstrate that the results of the proposed method are highly correlated with the subjective assessments. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/TCSVT.2014.2363737 | IEEE Trans. Circuits Syst. Video Techn. |
Keywords | Field | DocType |
h.264/avc,neural network,no-reference video quality prediction,artifact measurement,frame-level features,compressed natural videos,full-reference metrics,temporal pooling,noreference measure,discrete cosine transform,statistical analysis,regression analysis,trained multilayer,distortion measurement,nonlinear mapping,video quality assessment,natural scene,discrete cosine transforms,video quality assessment (vqa),support-vector regression,feature extraction,discrete cosine transform (dct),no-reference video quality assessment,image sequences,h.264/advanced video coding (avc),no-reference (nr) measure,dct,unsigned ac band,blocking artifact,support vector machines,neural nets,video-level feature,video sequence,neural networks,nonlinear distortion,support vector regression | Computer vision,Pattern recognition,Computer science,Linear model,Pooling,Discrete cosine transform,Feature extraction,Artificial intelligence,Artificial neural network,Nonlinear distortion,Video quality,Distortion | Journal |
Volume | Issue | ISSN |
25 | 4 | 1051-8215 |
Citations | PageRank | References |
19 | 0.65 | 34 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kongfeng Zhu | 1 | 22 | 1.36 |
| Chengqing Li | 2 | 19 | 0.65 |
| Vijayan K. Asari | 3 | 782 | 107.90 |
| Dietmar Saupe | 4 | 1104 | 85.80 |