Felix C. A. Fernandes

The inordinate power consumption of contemporary display panels accelerates battery depletion in mobile devices. Existing display-power reduction approaches are suboptimal because they incur additional computations and latency on the mobile device or because they assume a server-client model in which a single entity controls the server and the client. To overcome these limitations, we extract standardized metadata at a server and transmit it to a client along with the video bitstream. This metadata includes quality indicators, associated power controls, and contrast-enhancement information. The metadata guarantees that specified quality levels are achieved while avoiding flicker and minimizing power consumption. Furthermore, the metadata has minimal computational, storage & power overheads and, because it is standardized, the metadata benefits servers and clients in open ecosystems wherein all entities function independently. Our research has been implemented on mobile devices and provides, on average, 32.5 percent power reduction at high quality and up to 84 percent power reduction at acceptable quality. The ISO/IEC Moving Pictures Expert Group has recently standardized our proposed metadata in the Green Metadata Standard.

Super resolution (SR) for real-life video sequences is a challenging problem due to complex nature of the motion fields. In this paper, a novel blind SR method is proposed to improve the spatial resolution of video sequences, while the overall point spread function of the imaging system, motion fields, and noise statistics are unknown. To estimate the blur(s), first, a nonuniform interpolation SR method is utilized to upsample the frames, and then, the blur(s) is(are) estimated through a multiscale process. The blur estimation process is initially performed on a few emphasized edges and gradually on more edges as the iterations continue. Also for faster convergence, the blur is estimated in the filter domain rather than the pixel domain. The high-resolution frames are estimated using a cost function that has the fidelity and regularization terms of type Huber-Markov random field to preserve edges and fine details. The fidelity term is adaptively weighted at each iteration using a masking operation to suppress artifacts due to inaccurate motions. Very promising results are obtained for real-life videos containing detailed structures, complex motions, fast-moving objects, deformable regions, or severe brightness changes. The proposed method outperforms the state of the art in all performed experiments through both subjective and objective evaluations. The results are available online at http://lyle.smu.edu/~rajand/Video_SR/.