A Method for Single-Stimulus Quality Assessment of Segmented Video
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A Method for Single-Stimulus Quality Assessment of Segmented Video R. Piroddi1 and T. Vlachos2 1 Department
of Electrical and Electronic Engineering, Imperial College London, Exhibition Road, London SW7 2AZ, UK for Vision, Speech and Signal Processing (CVSSP), School of Electronics and Physical Sciences, University of Surrey, Guildford GU2 7XH, UK
2 Centre
Received 17 March 2005; Revised 11 July 2005; Accepted 31 July 2005 We present a unified method for single-stimulus quality assessment of segmented video. This method takes into consideration colour and motion features of a moving sequence and monitors their changes across segment boundaries. Features are estimated using a local neighbourhood which preserves the topological integrity of segment boundaries. Furthermore the proposed method addresses the problem of unreliable and/or unavailable feature estimates by applying normalized differential convolution (NDC). Our experimental results suggest that the proposed method outperforms competing methods in terms of sensitivity as well as noise immunity for a variety of standard test sequences. Copyright © 2006 Hindawi Publishing Corporation. All rights reserved.
1.
INTRODUCTION
Object-based descriptions of still images and moving sequences are becoming increasingly important for multimedia and broadcasting applications offering many welldocumented advantages [1]. Such descriptions allow the authoring, manipulation, editing, and coding of digital imagery in a far more creative, intuitive, efficient, and user-friendly manner compared to conventional frame-based alternatives. A key tool towards the identification of objects or regions of interest is segmentation which has emerged as a very active area of research in the past 20 years. Segmentation has often been regarded as a first step towards automated image analysis with applications in scene interpretation, object recognition, and compression, especially in view of the fact that it was shown to be well tuned to the characteristics of human vision. Despite its potential usefulness, segmentation is a fundamentally ill-posed problem and, as a consequence, generic non-application-specific solutions have remained elusive [2]. Additionally, a critical factor which has prevented any particular algorithm from gaining wider acceptance has been the lack of a unified method for the quality assessment of segmented imagery. While such assessment has traditionally relied on subjective means, it is self-evident that the development of an objective evaluation methodology holds the key to further advances in the field. In Figure 1, a classification of quality assessment methods for video object-based segmentation is shown. Reference
methods require ground-truth information as opposed to no-reference methods, which have no such requirement. Noreference methods can be further subdivided to interframe, where the temporal consistency of segmentation from one frame to another is taken into consideration, and intraframe, where this is not an issue. In relation to the assessment of still
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