Published on Feb 21, 2020
The PIVOT VECTOR SPACE APPROACH is a novel technique of audio-video mixing which automatically selects the best audio clip from the available database, to be mixed with the given video shot.
Till the development of this technique, audio-video mixing is a process that could be done only by professional audio-mixing artists. However employing these artists is very expensive and is not feasible for home video mixing. Besides, the process is time-consuming and tedious.
In today's era, significant advances are happening constantly in the field of Information Technology. The development in the IT related fields such as multimedia is extremely vast. This is evident with the release of a variety of multimedia products such as mobile handsets, portable MP3 players, digital video camcorders, handicams etc. Hence, certain activities such as production of home videos is easy due to products such as handicams, digital video camcorders etc. Such a scenario was not there a decade ago ,since no such products were available in the market. As a result production of home videos is not possible since it was reserved completely for professional video artists.
So in today's world, a large amount of home videos are being made and the number of amateur and home video enthusiasts is very large.A home video artist can never match the aesthetic capabilities of a professional audio mixing artist. However employing a professional mixing artist to develop home video is not feasible as it is expensive, tedious and time consuming.
The PIVOT VECTOR SPACE APPROACH is a technique that all amateur and home video enthusiasts can use in the creation of video footage that gives a professional look and feel. This technique saves cost and is fast. Since it is fully automatic, the user need not worry about his aesthetic capabilities. The PIVOT VECTOR SPACE APPROACH uses a pivot vector space mixing framework to incorporate the artistic heuristics for mixing audio with video .These artistic heuristics use high level perceptual descriptors of audio and video characteristics. Low-level signal processing techniques compute these descriptors.
The table shows, from the cinematic point of view,a set of attributed features(such as color and motion) required to describe videos.The computations for extracting aesthetic attributed features from low-level video features occur at the video shot granularity.
Because some attributed features are based on still images(such as high light falloff),we compute them on the key frame of a video shot. We try to optimize the trade-off in accuracy and computational efficiency among the competing extraction methods. Also, even though we assume that the videos considered come in the MPEG format(widely used by several home video camcorders),the features exist independently of a particular representation format.
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