4 research outputs found

    PIM: Video Coding using Perceptual Importance Maps

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    Human perception is at the core of lossy video compression, with numerous approaches developed for perceptual quality assessment and improvement over the past two decades. In the determination of perceptual quality, different spatio-temporal regions of the video differ in their relative importance to the human viewer. However, since it is challenging to infer or even collect such fine-grained information, it is often not used during compression beyond low-level heuristics. We present a framework which facilitates research into fine-grained subjective importance in compressed videos, which we then utilize to improve the rate-distortion performance of an existing video codec (x264). The contributions of this work are threefold: (1) we introduce a web-tool which allows scalable collection of fine-grained perceptual importance, by having users interactively paint spatio-temporal maps over encoded videos; (2) we use this tool to collect a dataset with 178 videos with a total of 14443 frames of human annotated spatio-temporal importance maps over the videos; and (3) we use our curated dataset to train a lightweight machine learning model which can predict these spatio-temporal importance regions. We demonstrate via a subjective study that encoding the videos in our dataset while taking into account the importance maps leads to higher perceptual quality at the same bitrate, with the videos encoded with importance maps preferred 1.8Ă—1.8 \times over the baseline videos. Similarly, we show that for the 18 videos in test set, the importance maps predicted by our model lead to higher perceptual quality videos, 2Ă—2 \times preferred over the baseline at the same bitrate

    Computational integrity with a public random string from quasi-linear PCPs

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    A party running a computation remotely may benefit from misreporting its output, say, to lower its tax. Cryptographic protocols that detect and prevent such falsities hold the promise to enhance the security of decentralized systems with stringent computational integrity requirements, like Bitcoin [Nak09]. To gain public trust it is imperative to use publicly verifiable protocols that have no “backdoors” and which can be set up using only a short public random string. Probabilistically Checkable Proof (PCP) systems [BFL90, BFLS91, AS98, ALM + 98] can be used to construct astonishingly efficient protocols [Kil92, Mic00] of this nature but some of the main components of such systems — proof composition [AS98] and low-degree testing via PCPs of Proximity (PCPPs) [BGH + 05, DR06] — have been considered efficient only asymptotically, for unrealistically large computations; recent cryptographic alternatives [PGHR13, BCG + 13a] suffer from a non-public setup phase. This work introduces SCI, the first implementation of a scalable PCP system (that uses both PCPPs and proof composition). We used SCI to prove correctness of executions of up to 2202^{20} cycles of a simple processor (Figure 1) and calculated (Figure 2) its break-even point [SVP + 12, SMBW12]. The significance of our findings is two-fold: (i) it marks the transition of core PCP techniques (like proof composition and PCPs of Proximity) from mathematical theory to practical system engineering, and (ii) the thresholds obtained are nearly achievable and hence show that PCP-supported computational integrity is closer to reality than previously assumed
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