A model for evaluating the effectiveness of automatic recording of television programs by digital personal video recorders (PVRs) is presented. The model is used to evaluate the tradeoff between manual management of recording programs to a PVR archive and automatic management of recording. We show that a tradeoff exists between manual management in which the utility of a program is perfectly known but user awareness of available programs is limited versus
automatic management in which utility estimates contain error but awareness is perfect. Experiments with the model show that the shape of this tradeoff is most governed by the shape of the distribution of user utility. As the percentage of programs with high user utility decreases relative to the average, the more effective automatic recording is likely to be despite errors in utility estimation. The shape of this tradeoff, however, is highly inelastic. Thus, improving utility estimates will not make automatic recording more effective if user awareness is sufficiently high.
The JPEG compression standard is a popular image format. However, at high compression ratios JPEG compression, which uses block-transform coding, can produce blocking artifacts, or artificially introduced edges within the image. Several post-processing algorithms have been developed to remove these artifacts. This paper describes an implementation of a post-processing algorithm developed by Ramchandran, Chou, and Crouse (RCC) which is fast enough for real-time software-only video applications. The original implementation of the RCC algorithm involved calculating thresholds to identify artificial edges. These calculations proved too expensive for use in real-time software-only applications. We replaced these calculations with a linear scale approximating ideal threshold values based on a combination of peak signal-to-noise ratio calculations and subjective visual quality. The resulting filter implementation is available in the widely-deployed Open Mash streaming media toolkit.