No-reference video quality measurement: added value of machine learning

Abstract

Video quality measurement is an important component in the end-to-end video delivery chain. Video quality is, however, subjective, and thus, there will always be interobserver differences in the subjective opinion about the visual quality of the same video. Despite this, most existing works on objective quality measurement typically focus only on predicting a single score and evaluate their prediction accuracies based on how close it is to the mean opinion scores (or similar average based ratings). Clearly, such an approach ignores the underlying diversities in the subjective scoring process and, as a result, does not allow further analysis on how reliable the objective prediction is in terms of subjective variability. Consequently, the aim of this paper is to analyze this issue and present a machine-learning based solution to address it. We demonstrate the utility of our ideas by considering the practical scenario of video broadcast transmissions with focus on digital terrestrial television (DTT) and proposing a no-reference objective video quality estimator for such application. We conducted meaningful verification studies on different video content (including video clips recorded from real DTT broadcast transmissions) in order to verify the performance of the proposed solution.Downloaded From: http://electronicimaging.spiedigitallibrary.org/ on 01/02/2016 Terms of Use: http://spiedigitallibrary.org/ss/TermsOfUse.aspx Accuracy (in general) Higher than RR and NR. Higher than NR; lower than FR. Lower than FR and RR.

Spatiotemporal Feature Integration and Model Fusion for Full Reference Video Quality Assessment

“…Data-driven models hold great promise for the VQA problem [12], [15], [27], [37]- [40]. Netflix recently announced the Video Multimethod Fusion Approach (VMAF), which is an open-source, learning-based FR VQA model.…”

Data Analysis in Multimedia Quality Assessment: Revisiting the Statistical Tests

“…For instance, the authors in [22] proposed the use of SOS (standard deviation of opinion scores) while Ref. [23] suggested using PDU (percentage dissatisfied users) in addition to MOS. Note that measures such as SOS, PDU can be different even if corresponding population MOS are equal.…”

Synopsis of the PhD Thesis - Network Computations in Artificial Intelligence

“…2) Objective video quality assessment: For obvious reasons, video quality assessment, is more difficult and more important than image quality assessment [52], [53]. In [54]- [58] we take further our work on images, proposing new no-reference and reduced-reference QoE methods to assess the quality degradation suffered by videos during streaming services. We use various models of artificial neural networks, from restricted Boltzmann machines to deep neural networks, using both unsupervised and supervised learning.…”