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We investigate video classification via a two-stream convolutional neural network (CNN) design that directly ingests information extracted from compressed video bitstreams. Our approach begins with the observation that all modern video codecs divide the input frames into macroblocks (MBs). We demonstrate that selective access to MB motion vector (MV) information within compressed video bitstreams can also provide for selective, motion-adaptive, MB pixel decoding (a.k.a., MB texture decoding). This in turn allows for the derivation of spatio-temporal video activity regions at extremely high speed in comparison to conventional full-frame decoding followed by optical flow estimation. In order to evaluate the accuracy of a video classification framework based on such activity data, we independently train two CNN architectures on MB texture and MV correspondences and then fuse their scores to derive the final classification of each test video. Evaluation on two standard data sets shows that the proposed approach is competitive with the best two-stream video classification approaches found in the literature. At the same time: 1) a CPU-based realization of our MV extraction is over 977 times faster than GPU-based optical flow methods; 2) selective decoding is up to 12 times faster than full-frame decoding; and 3) our proposed spatial and temporal CNNs perform inference at 5 to 49 times lower cloud computing cost than the fastest methods from the literature. [ABSTRACT FROM AUTHOR]

A video compression framework based on spatio-temporal resolution adaptation (ViSTRA) is proposed, which dynamically resamples the input video spatially and temporally during encoding, based on a quantisation-resolution decision, and reconstructs the full resolution video at the decoder. Temporal upsampling is performed using frame repetition, whereas a convolutional neural network super-resolution model is employed for spatial resolution upsampling. ViSTRA has been integrated into the high efficiency video coding reference software (HM 16.14). Experimental results verified via an international challenge show significant improvements, with BD-rate gains of 15% based on PSNR and an average MOS difference of 0.5 based on subjective visual quality tests. [ABSTRACT FROM AUTHOR]

Recently, multiple ($k, n$) region-incrementing VCSs (RIVCSs) have been proposed that can gradually reconstruct secrets in a single image. In ($k, n$)-RIVCS, the secret image is subdivided into multiple regions in such a way that any $t$ shadow images, where $k \le t \le n$ , can be used to reveal the secret in the ($t-k+1$)th region. This region-incrementing property provides progressive decoding. However, the regions in previous RIVCSs are disjointed: no two regions have overlapping areas. In this paper, we discuss a ($k, n$) region-in-region progressive VCS (RPVCS). Our ($k, n$)-RPVCS has ($n-k+1$) secrecy-level regions, and each region can be located within any previous region. This new type of region allocation not only provides more areas in which to hide the secret compared with the non-overlapping regions in RIVCS but also provides different presentation methods in progressive decoding. [ABSTRACT FROM AUTHOR]

This paper provides an overview of Scalable High efficiency Video Coding (SHVC), the scalable extensions of the High Efficiency Video Coding (HEVC) standard, published in the second version of HEVC. In addition to the temporal scalability already provided by the first version of HEVC, SHVC further provides spatial, signal-to-noise ratio, bit depth, and color gamut scalability functionalities, as well as combinations of any of these. The SHVC architecture design enables SHVC implementations to be built using multiple repurposed single-layer HEVC codec cores, with the addition of interlayer reference picture processing modules. The general multilayer high-level syntax design common to all multilayer HEVC extensions, including SHVC, MV-HEVC, and 3D HEVC, is described. The interlayer reference picture processing modules, including texture and motion resampling and color mapping, are also described. Performance comparisons are provided for SHVC versus simulcast HEVC and versus the scalable video coding extension to H.264/advanced video coding. [ABSTRACT FROM PUBLISHER]