Video Quality Pooling Adaptive to Perceptual Distortion Severity

Park, Jincheol; Seshadrinathan, Kalpana; Lee, Sang Hoon; Bovik, Alan C.

doi:10.1109/tip.2012.2219551

Cited by 120 publications

(57 citation statements)

References 42 publications

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“…When the max, min, and operator are used without indexing, indexing over the values for the I-frames is assumed. Motivated by [14], clustering is performed [15] on some information from the codec analysis values to obtain a cluster with general high values and a cluster with general low values, which is denoted by C h (·) and C l (·), respectively. This can be used to calculate a weighted average:…”

Section: Video Analysismentioning

confidence: 99%

Video quality assessment and machine learning: Performance and interpretability

Sogaard

Forchhammer

Korhonen

2015

2015 Seventh International Workshop on Quality of Multimedia Experience (QoMEX)

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Abstract-In this work we compare a simple and a complex Machine Learning (ML) method used for the purpose of Video Quality Assessment (VQA). The simple ML method chosen is the Elastic Net (EN), which is a regularized linear regression model and easier to interpret. The more complex method chosen is Support Vector Regression (SVR), which has gained popularity in VQA research. Additionally, we present an ML-based feature selection method. Also, it is investigated how well the methods perform when tested on videos from other datasets. Our results show that content-independent cross-validation performance on a single dataset can be misleading and that in the case of very limited training and test data, especially in regards to different content as is the case for many video datasets, a simple ML approach is the better choice.

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Section: Video Analysismentioning

confidence: 99%

Video quality assessment and machine learning: Performance and interpretability

Sogaard

Forchhammer

Korhonen

2015

2015 Seventh International Workshop on Quality of Multimedia Experience (QoMEX)

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“…We observe that most of the pixel domain features-based approaches have been designed for images and it is desirable to generalize the relevant methods for applications in the case of videos. Temporal pooling quality methods such as Minkowski summation or other methods such as adaptive to perceptual distortion [136] can be used for this purpose. Table 2 presents a summary of some of the methods discussed in this section.…”

Section: Discussionmentioning

confidence: 99%

No-reference image and video quality assessment: a classification and review of recent approaches

et al. 2014

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The field of perceptual quality assessment has gone through a wide range of developments and it is still growing. In particular, the area of no-reference (NR) image and video quality assessment has progressed rapidly during the last decade. In this article, we present a classification and review of latest published research work in the area of NR image and video quality assessment. The NR methods of visual quality assessment considered for review are structured into categories and subcategories based on the types of methodologies used for the underlying processing employed for quality estimation. Overall, the classification has been done into three categories, namely, pixel-based methods, bitstream-based methods, and hybrid methods of the aforementioned two categories. We believe that the review presented in this article will be helpful for practitioners as well as for researchers to keep abreast of the recent developments in the area of NR image and video quality assessment. This article can be used for various purposes such as gaining a structured overview of the field and to carry out performance comparisons for the state-of-the-art methods. Keywords:No-reference; Image quality assessment; Video quality assessment; Perceptual quality 1 Review IntroductionThere has been a tremendous progress recently in the usage of digital images and videos for an increasing number of applications. Multimedia services that have gained wide interest include digital television broadcasts, video streaming applications, and real-time audio and video services over the Internet. The global mobile data traffic grew by 81% in 2013, and during 2014, the number of mobileconnected devices will exceed the number of people on earth, according to predictions made by Cisco. The video portion of the mobile data traffic was 53% in 2013 and is expected to exceed 67% by 2018 [1]. With this huge increase in the exposure of image and video to the human eye, the interest in delivering quality of experience (QoE) may increase naturally. The quality of visual media can get degraded during capturing, compression, transmission, reproduction, and displaying due to the distortions that might occur at any of these stages. The legitimate judges of visual quality are humans as end users, the opinions of whom can be obtained by subjective experiments. Subjective experiments involve a panel of participants which are usually non-experts, also referred to as test subjects, to assess the perceptual quality of given test material such as a sequence of images or videos. Subjective experiments are typically conducted in a controlled laboratory environment. Careful planning and several factors including assessment method, selection of test material, viewing conditions, grading scale, and timing of presentation have to be considered prior to a subjective experiment. For example, Recommendation (ITU-R) BT.500 [2] provides detailed guidelines for conducting subjective experiments for the assessment of quality of television pictures. The outcomes of a subjectiv...

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“…Since the perception of quality also depends on recency [1], we also calculate the mean of the metric scores corresponding to the first and last 2 seconds of the video, respectively. Finally, inspired by [7] we divide the objective scores into clusters depending on the image metric scores using the method from [8], such that we obtain clusters of varying quality levels. Then a weighted average of the objective score from the clusters with lowest µ L and highest means µ H are calculated as:…”

Section: Image Metrics and Video Featuresmentioning

confidence: 99%

Quality assessment of adaptive bitrate videos using image metrics and machine learning

Sogaard

Forchhammer

Brunnström

2015

2015 Seventh International Workshop on Quality of Multimedia Experience (QoMEX)

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Abstract-Adaptive bitrate (ABR) streaming is widely used for distribution of videos over the internet. In this work, we investigate how well we can predict the quality of such videos using well-known image metrics, information about the bitrate levels, and a relatively simple machine learning method. Quality assessment of ABR videos is a hard problem, but our initial results are promising. We obtain a Spearman rank order correlation of 0.88 using content-independent cross-validation.

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Video Quality Pooling Adaptive to Perceptual Distortion Severity

Cited by 120 publications

References 42 publications

Video quality assessment and machine learning: Performance and interpretability

Video quality assessment and machine learning: Performance and interpretability

No-reference image and video quality assessment: a classification and review of recent approaches

Quality assessment of adaptive bitrate videos using image metrics and machine learning

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