With multicore architectures being introduced to the market, the research community is revisiting problems to evaluate them under the new preconditions set by those new systems. Algorithms need to be implemented with scalability in mind. One problem that is known to be computationally demanding is video decoding. In this paper, we will present a technique that increases the scalability of H.264 video decoding by modifying only the encoder stage. In embedded scenarios, increased scalability can also enable reduced clock speeds of the individual cores, thus lowering overall power consumption.The key idea is to equalize the potentially differing decoding times of one frame's slices by applying decoding time prediction at the encoder stage. Virtually no added penalty is inflicted on the quality or size of the encoded video. Because decoding times are predicted rather than measured, the encoder does not rely on accurate timing and can therefore run as a batch job on an encoder farm as is current practice today. In addition, apart from a decoder capable of slice-parallel decoding, no changes to the installed client systems are required, because the resulting bitstreams will still be fully compliant to the H.264 standard.Consequently, this paper also contributes a way to accurately predict H.264 decoding times with average relative errors down to 1 %.

No abstract

No abstract

No abstract

In this article we present three key ideas which together form a flexible framework for maximizing user-perceived quality under given resources with modern video codecs (H.264). First, we present a method to predict resource usage for video decoding online. For this, we develop and discuss a video decoder model using key metadata from the video stream. Second, we explain a light-weight method for providing replacement content for a given region of a frame. We use this method for online adaptation. Third, we select a metric modeled after human image perception which we extend to quantify the consequences of available online adaptation decisions. Together, these three parts allow us, to the best of our knowledge for the first time, to maximize user-perceived quality in video playback under given resource constraints.