Hybrid video quality prediction: reviewing video quality measurement for widening application scope

Barkowsky, Marcus; Sedano, Inigo; Brunnström, Kjell; Leszczuk, Mikołaj; Staelens, Nicolas

doi:10.1007/s11042-014-1978-2

Cited by 14 publications

(10 citation statements)

References 50 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…• In order to combine independent and isolated approaches for the development of hybrid NR VQA methods, a five-point agenda has been identified by joint effort group (JEG) at VQEG [202]. We believe that such collaborative works will be instrumental in paving the ways of NR VQA towards a measurable evolution.…”

Section: Discussionmentioning

confidence: 99%

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

et al. 2014

View full text Add to dashboard Cite

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...

show abstract

Section: Discussionmentioning

confidence: 99%

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

et al. 2014

View full text Add to dashboard Cite

show abstract

“…One of the underlying reasons of the limitation of the scope of a metric is related to design-requirements taken into consideration at the time of development [2], [3]. This limitation is very often not clear or not known for subsequent users of the metrics.…”

Section: Introductionmentioning

confidence: 99%

Applicability of Existing Objective Metrics of Perceptual Quality for Adaptive Video Streaming

Sogaard¹,

Krasula²,

Shahid³

et al. 2016

Self Cite

View full text Add to dashboard Cite

Objective video quality metrics are designed to estimate the quality of experience of the end user. However, these objective metrics are usually validated with video streams degraded under common distortion types. In the presented work, we analyze the performance of published and known full-reference and noreference quality metrics in estimating the perceived quality of adaptive bit-rate video streams knowingly out of scope. Experimental results indicate not surprisingly that state of the art objective quality metrics overlook the perceived degradations in the adaptive video streams and perform poorly in estimating the subjective quality results.

show abstract

“…Depending on whether the full reference video is available for measure- A recent overview of video quality algorithms including a sophisticated categorization can be found in [8]. In [9], the authors focus on identification of internal indicators used by some well-known algorithms such that they may be exploited independently to widen the scope of application. Indicators should measure correctly when changes inside their scope of application appear but should stay neutral when the changes do not concern their measurement specificity, for example, a measure for annoyance of irregular frame skips should not be affected by a reduced frame rate.…”

Section: Introductionmentioning

confidence: 99%

Objective Video Quality Assessment — Towards Large Scale Video Database Enhanced Model Development

Barkowsky

Masala

Wallendael

et al. 2015

IEICE Trans. Commun.

Self Cite

View full text Add to dashboard Cite

SUMMARYThe current development of video quality assessment algorithms suffers from the lack of available video sequences for training, verification and validation to determine and enhance the algorithm's application scope. The Joint Effort Group of the Video Quality Experts Group (VQEG-JEG) is currently driving efforts towards the creation of large scale, reproducible, and easy to use databases. These databases will contain bitstreams of recent video encoders (H.264, H.265), packet loss impairment patterns and impaired bitstreams, pre-parsed bitstream information into files in XML syntax, and well-known objective video quality measurement outputs. The database is continuously updated and enlarged using reproducible processing chains. Currently, more than 70,000 sequences are available for statistical analysis of video quality measurement algorithms. New research questions are posed as the database is designed to verify and validate models on a very large scale, testing and validating various scopes of applications, while subjective assessment has to be limited to a comparably small subset of the database. Special focus is given on the principles guiding the database development, and some results are given to illustrate the practical usefulness of such a database with respect to the detailed new research questions. key words: video quality assessment, large scale database, reproducible research

show abstract

Hybrid video quality prediction: reviewing video quality measurement for widening application scope

Cited by 14 publications

References 50 publications

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

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

Applicability of Existing Objective Metrics of Perceptual Quality for Adaptive Video Streaming

Objective Video Quality Assessment — Towards Large Scale Video Database Enhanced Model Development

Contact Info

Product

Resources

About