Guest editors' introduction: Perception, Aesthetics, and Emotion in Multimedia Quality Modeling
نویسندگان
چکیده
T he volume of multimedia data handled daily is growing rapidly due to cheap and ubiquitous sensors and sharing platforms and our increasing use of social media. Computational quality modeling is thus becoming an important yet challenging research topic. Artificial intelligence has proven effective in interpreting and managing this preponderance of data, and in the past decade, a rich variety of quality models have been proposed. A series of multimedia applications have benefited from such models, including photo-retargeting, imageretrieval, and speech-recognition applications. Defining a Quality Model Media quality is a relatively broad concept, and the meaning of it depends on the specific application. For example, in the image-segmentation domain, a quality model is used to measure the relationship of two or more superpixels. This quality measurement is very important in deciding whether two or more superpixels can be merged. In photo/video retargeting, the quality model is deployed to measure the importance of each region prior to shrinking the image— “important” regions aren’t reduced as much as the “unimportant” regions. The quality model also plays an important role in video summarization, deciding whether a set of video frames should be condensed into a single one. Many new computational quality models are proposed each year. However, effective tools for manipulating the quality prediction of media content are still in their infancy. Building a successful quality model depends on a wide range of components, including signal processing, multimedia semantic modeling, and human perception simulation. Several technical challenges exist:
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ورودعنوان ژورنال:
- IEEE MultiMedia
دوره 23 شماره
صفحات -
تاریخ انتشار 2016