Automated Visual Quality Analysis for Media Production

Automatic quality control for audiovisual media is an important tool in the media production process. In this paper we present tools for assessing the quality of audiovisual content in order to decide about the reusability of archive content. We first discuss automatic detectors for the common impairments noise and grain, video breakups, sharpness, image dynamics and blocking. For the efficient viewing and verification of the automatic results by an operator, three approaches for user interfaces are presented. Finally, we discuss the integration of the tools into a service oriented architecture, focusing on the recent standardization efforts by EBU and AMWA's Joint Task Force on a Framework for Interoperability of Media Services in TV Production (FIMS).

[1]  E. Peli Contrast in complex images. , 1990, Journal of the Optical Society of America. A, Optics and image science.

[2]  Jon Y. Hardeberg,et al.  Attributes of image quality for color prints , 2010, J. Electronic Imaging.

[3]  John Footen,et al.  What is the Service-Oriented Media Enterprise? , 2008 .

[4]  Stefan Eickeler,et al.  A new quality assessment and improvement system for print media , 2012, EURASIP J. Adv. Signal Process..

[5]  J. Kulikowski,et al.  Pattern and flicker detection analysed by subthreshold summation. , 1975, The Journal of physiology.

[6]  Gary J. Sullivan,et al.  Rate-constrained coder control and comparison of video coding standards , 2003, IEEE Trans. Circuits Syst. Video Technol..

[7]  Stefan Winkler,et al.  A no-reference perceptual blur metric , 2002, Proceedings. International Conference on Image Processing.

[8]  Lina J. Karam,et al.  A No-Reference Objective Image Sharpness Metric Based on the Notion of Just Noticeable Blur (JNB) , 2009, IEEE Transactions on Image Processing.

[9]  Albert A. Michelson,et al.  Studies in Optics , 1995 .

[10]  Ajay Luthra,et al.  Overview of the H.264/AVC video coding standard , 2003, IEEE Trans. Circuits Syst. Video Technol..

[11]  Zhou Wang,et al.  Modern Image Quality Assessment , 2006, Modern Image Quality Assessment.

[12]  Martin Winter,et al.  Efficient video breakup detection and verification , 2010, AIEMPro '10.

[13]  Patrick Ndjiki-Nya,et al.  A new perceptual-based no-reference contrast metric for natural images based on human attention and image dynamic , 2012, 2012 Fourth International Workshop on Quality of Multimedia Experience.

[14]  Martin Winter,et al.  Real-time video breakup detection for multiple HD video streams on a single GPU , 2012, Real-Time Image and Video Processing.