Real Time Semantic Adaptation of Sports Video with User-centred Performance Analysis

Abstract: Semantic video adaptation improves traditional adaptation by taking into account the degree of relevance of the different portions of the content. It employs solutions to detect the significant parts of the video and applies different compression ratios to elements that have different importance. Performance of semantic adaptation heavily depends on the quality and precision of the automatic annotation, whether it operates in strict or nonstrict real time, and the codec which is used to perform adaptation at the event or object level. It should consider the effects of the errors in the automatic extraction of objects and events over the operation of the adaptation subsystem, and relate these effects to the preferences for the objects and events of the video program, that have been decided by the user. In this paper, we present strict real time annotation and adaptation of sports video and introduce two new performance measures: Viewing Quality Loss and Bit-rate Cost Increase, that are obtained from classical PSNR and Bit Ratio, but relate the results of semantic adaptation with the user’s preferences and expectations.

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