Semantic video adaptation based on automatic annotation of sport videos

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 precision of the automatic annotation and the way of operation of the codec which is used to perform adaptation at the event or object level. In this paper, we discuss critical factors that affect performance of automatic annotation and define new performance measures of semantic adaptation, Viewing Quality Loss and Bitrate Cost Increase, that are obtained from classical PSNR and Bit Rate, but relate the results of semantic adaptation with the user's preferences and expectations. The new measures are discussed in detail for a system of sport annotation and adaptation with reference to different user profiles