| With the increasing communication of multimedia data over the Internet, and the increasing ubiquity of wireless devices, it is essential to enable the communication of image and video data over wireless channels. Communication over wireless channels, however, face several problems, such as energy consumption, bandwidth, and noise, that are distinct from the problems faced to enable wired communication. To address the bandwidth, noise, and energy problems, this dissertation proposes robust and cost-efficient algorithms and architectures that enable the shaping of image and video data for wireless communication.; To enable the shaping of image data, we explore parameters used for shaping data within the JPEG image compression algorithm. We then propose an algorithm for selecting what parameters will minimize the total energy dissipation when compressing and transmitting an image over a wireless channel.; Two video shaping methods are also proposed to help overcome the bandwidth and noise bottlenecks to wireless video communication. To help overcome the effects of noise, a novel buffering algorithm, ORBit, is proposed that introduces temporal diversity to short video clips, thereby minimizing the video quality degradation due to noise in a wireless channel. We also introduce VShaper, a method for adjusting a streaming video to the current bandwidth and noise conditions of the wireless channel. By matching the streamed video to the current wireless conditions, a high video quality is achieved across a variety of wireless conditions.; To enable complex image and video shaping algorithms, we also address the architectural issues associated with enabling adaptive data shaping. Architectures for image and video shaping must be configurable to enable adaptation, while consuming minimal power and achieving the computational performance needed for the shaping algorithms. An architecture is introduced that, in addition to being low-power, enables adaptation across multiple image compression algorithms and their parameters. Using this architecture results in significant energy savings over an all-software implementation. To enable the development of low-power architectures in deep sub-micron technology, we propose a new energy modeling and minimization methodology for energy dissipation in deep sub-micron technologies. |
