The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
Dictionary learning has emerged as a promising alternative to the conventional hybrid coding framework. However, the rigid structure of sequential training and prediction degrades its performance in scalable video coding. This paper proposes a progressive dictionary learning framework with hierarchical predictive structure for scalable video coding, especially in low bitrate region. For pyramidal...
To enable learning-based video coding for transmission over heterogenous networks, this paper proposes a scalable video coding framework by progressive dictionary learning. With the hierarchical B-picture prediction structure, the inter-predicted frames would be reconstructed in terms of the spatio-temporal dictionary in a successive sense. Within the progressive dictionary learning, the training...
In this paper, we propose a sparse representation learning with adaptive regularized dictionaries and develop a low bit-rate video coding scheme. In a reversed-complexity manner, it select a subset of key frames to encode at original resolution, while the rest are down-sampled and super-resolution reconstructed by a sparse super-resolution estimations using key frames as training set. Since primitive...
To correctly detect dynamic targets and obtain a record of the trajectories of identical targets in appearance over time, has become significantly more challenging and infers countless applications in biomedicine. In this paper, we propose a novel structured learning-based graph matching algorithm to track a variable number of interacting objects in dynamic environments. Different from previous approaches,...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.