The popularization of video capture devices has created strong storage demand for encoded videos. Approximate storage can ease this demand by enabling denser storage at the expense of occasional errors. Unfortunately, even minor storage errors, such as bit flips, can result in major visual damage in encoded videos. Similarly, video encryption, widely employed for privacy and digital rights management, may create long dependencies between bits that show little or no tolerance to storage errors.<\/p>\n
In this paper we propose VideoApp, a novel and efficient methodology to compute bit-level reliability requirements for encoded videos by tracking visual and metadata dependencies within encoded bitstreams. We further show how VideoApp can be used to trade video quality for storage density in an optimal way. We integrate our methodology into a popular H.264 encoder to partition an encoded video stream into multiple streams that can receive different levels of error correction according to their reliability needs. When applied to a dense and highly error-prone multi-level cell storage substrate, our variable error correction mechanism reduces the error correction overhead by half under the most error-intolerant encoder settings, achieving quality\/density points that neither compression nor approximation can achieve alone. Finally, we define the basic invariants needed to support encrypted approximate video storage. We present an analysis of block cipher modes of operation, showing that some are fully compatible with approximation, enabling approximate and secure video storage systems.<\/p>\n","protected":false},"excerpt":{"rendered":"
The popularization of video capture devices has created strong storage demand for encoded videos. Approximate storage can ease this demand by enabling denser storage at the expense of occasional errors. Unfortunately, even minor storage errors, such as bit flips, can result in major visual damage in encoded videos. 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