A workload prediction model for decoding mpeg video and its application to workload-scalable transcoding

Abstract

Multimedia playback is restricted by the processing power of mobile devices, and in particular, the playback quality can be degraded due to insufficient processing power. To address this problem, we propose a new workload-scalable transcoding scheme which converts a pre-recorded video bitstream into a new video bitstream that satisfies the device's workload constraint, while keeping the transcoding distortion minimal. The key of this proposed transcoding scheme lies on a new workload prediction model, which is fast, accurate and is generic enough to apply to different video formats, decoder implementations and target platforms. The main contributions of this paper include 1) a workload prediction model for decoding MPEG video based on an offline bitstream analysis method; 2) a transcoding scheme that uses the proposed model to control the decoding workload on the target device. To facilitate our transcoding scheme, we have proposed a compressed domain distortion measure (CDDM) that takes effects from both frames per second (fps) and bits per frame (bpf) into consideration. CDDM ensures the transcoded video bitstream to have the best playback quality given the device's workload constraint. Both the workload prediction model and the transcoding scheme are evaluated experimentally.

Predicting the throughput of multiprocessor applications under dynamic workload

“…We have to fill the set of scenario-specific reference schedules with vector valuesr spec (s) such that the scenario coefficients τ spec (s,t) induced by these schedules yield the minimal contribution in the third term of Eq. (9). Note that this is a particular case of the minimal-error coefficient optimization problem mentioned in Section III.A.…”

“…Examples of this process are resource allocation [3] and the management of limited system resources, e.g., quality scaling [9] and dynamic voltage scaling [8].…”

Power reduction via macroblock prioritization for power aware H.264 video applications

“…Additionally, reducing quantization levels in the decoder does not provide significant computational savings relative to block or frame dropping, and slice dropping provides better resolution version of frame dropping. [6] analyzes the video stream in the compressed domain, pruning the compressed data based on a model which estimates the effect on distortion. This scheme is aimed at mobile devices on a network where each device has limited computational ability considered by the video server which appropriately modifies the compressed data stream.…”

Hilbert transform based workload estimation for low power surveillance video compression

“…Workload estimation techniques have been developed for video decoding, such as MPEG-2 [2]- [5], MPEG-4 [5] and H.264/AVC decoding [6], because of the convenience in retrieving the information from the compressed bitstream, e.g. the number of skipped macroblocks, to eliminate the influence of content varieties.…”