This paper addresses the problem of compressing the colour attributes of dynamic dense point clouds. Inter-frame prediction and motion compensation are key to removing temporal redundancies and thereby reaching major compression gains. For that purpose, an attribute compression scheme combining intra-frame and motion-compensated inter-frame predictions is presented. It is built upon the Test Model developed by MPEG within the Geometry-based Point Cloud Compression (G-PCC) activity to encode dense dynamic point clouds. More precisely, a motion-compensated inter-frame prediction is introduced into the RAHT encoding scheme, leveraging the local motion field already present in the geometry encoder. The best prediction mode according to rate-distortion optimization is decided at each node of the octree, encoded by means of arithmetic coding using a binary prediction tree and signalled to the decoder. Experimental results are provided using the MPEG test sequences and common test conditions. They demonstrate very significant compression gains, with average BD-Rates of −15.2%, −18.3% and −18.1 % on Y, Cb and Cr colour components respectively, when compared with the current scheme with intra-frame only attribute compression.

In this paper we address the problem of view synthesis from large baseline light fields, by turning a sparse set of input views into a Multi-plane Image (MPI). Because available datasets are scarce, we propose a lightweight network that does not require extensive training. Unlike latest approaches, our model does not learn to estimate RGB layers but only encodes the scene geometry within MPI alpha layers, which comes down to a segmentation task. A Learned Gradient Descent (LGD) framework is used to cascade the same convolutional network in a recurrent fashion in order to refine the volumetric representation obtained. Thanks to its low number of parameters, our model trains successfully on a small light field video dataset and provides visually appealing results. It also exhibits convenient generalization properties regarding both the number of input views, the number of depth planes in the MPI, and the number of refinement iterations.

Camcorder piracy refers to the process of using a camcorder to record a screen that displays copyrighted content. In contrast to the previous works that aimed at detecting the occurrence of camcorder piracy, this paper conducts an in-depth study of the luminance flicker that is naturally present in camcorded videos due to the interplay between a liquid-crystal-display (LCD) screen and a camcorder. We first model the flicker signal and show that its parameters are tied to such internal characteristics of the pirate devices as the back-light frequency of the LCD screen and the read-out time of the camcorder. We then present new estimation techniques to recover these hidden parameters directly from camcorded videos and demonstrate that such forensic cues could provide intelligence on the pirate devices. We also discuss how to recover the shape of the low-power flicker signal itself and show that it could be used to infer which back-light technology employed in the pirate LCD screen.

Spatio-temporal desynchronization remains a major challenge for watermarking system as it could impair the detection of the hidden payload. Over the years, several (non-blind) registration techniques have been proposed to realign the analyzed content prior to watermark detection and thereby achieve robustness against severe attacks such as display-and-camcord. Such techniques rely on assumptions that may be invalidated by new attacks that had not been accounted for. In the case of video, for instance, best-in-class resynchronization techniques may have difficulties to cope with screencasting. In this paper, we report findings after an experimental study that involved five popular screencasting tools. The impact of screencasting can indeed be characterized in terms of frame drops and repeats. Moreover, the on-the-fly encoding process occurring during screen recapture inherently introduces dependencies between the likelihood of drop/repeat and the frame encoding type.

Cryptography-based content protection is an efficient means to protect multimedia content during transport. Nevertheless, content is eventually decrypted at rendering time, leaving it vulnerable to piracy e.g. using a camcorder to record movies displayed on an LCD screen. Such type of piracy naturally imprints a visible flicker signal in the pirate video due to the interplay between the rendering and acquisition devices. The parameters of such flicker are inherently tied to the characteristics of the pirate devices such as the back-light of the LCD screen and the read-out time of the camcorder. In this article, we introduce a forensic methodology to estimate such parameters by analyzing the flicker signal present in pirate recordings. Experimental results clearly showcase that the accuracy of these estimation techniques offers efficient means to tell-tale which devices have been used for piracy thanks to the variety of factory settings used by consumer electronics manufacturers.

Despite the academic focus on blind detection, many forensic watermarking systems actually operate in a non-blind fashion to accommodate for real-life desynchronization attacks. In other words, the original content is needed to perform watermark detection e.g. to re-align the pirate sample with the original content prior to watermark decoding. A somehow intermediate strategy, that could be coined `semi-blind', only requires access to a condensed representation of the original content i.e. content fingerprints. In this paper, we combined a variant of the dynamic time warping algorithm together with multi-dimensional temporal fingerprints to realign pirate video samples. Reported experimental results clearly demonstrate improved accuracy compared to state-of-the-art registration methods for a number of synthetic and real-life attack scenarios.

Designing watermarks that can withstand geometric and temporal attacks is a notoriously difficult problem. The registration of a pirate content with the reference content, prior to the decoding of an embedded forensic mark, is therefore preferred. Semi-automatic registration systems, requiring the expert user to manually select and match control points through a suitable user interface, are not acceptable for content owners and do not usually provide very accurate results. A fully automated registration scheme is therefore chosen to synchronize pirate and master contents. By extracting robust and unique features, digital fingerprinting provides a relevant framework for automatic semi-blind content registration. In this paper we focus on temporal registration. In order to guarantee an accurate alignment while keeping the fingerprint size as small as possible, we propose a temporally adaptive approach. The registration scheme is designed around an adaptive motion description based on hierarchical encoding of the wavelet coefficients computed on the difference of successive frames.

In this industrial exhibit, Technicolor will showcase various technologies developed to protect digital audio-visual material all along the media value chain, from production to distribution, including forensics services in case pirated content is detected. The exhibit will be organized around Technicolor's proprietary technologies in video fingerprinting and audio/video watermarking.

This paper presents a framework for the forensic analysis of video content, for example those illegal copies of feature films proliferating in peer-to-peer networks or on bootleg DVDs. Compact and discriminant feature vectors, so called ¿fingerprints¿, are computed for a catalog of master contents. They combine global (image-based) and local (keypoint-based) signal properties extracted on representative keyframes, together with a one-dimensional signature capturing the temporal evolution of the video. We demonstrate that the extracted fingerprints not only allow accurate copy detection but also enable precise temporal and geometric alignment of candidate videos with master content. Besides making possible watermark decoding in the presence of geometric distortion, this opens the door to the reconstruction of the history of signal manipulations. The performance of the proposed scheme is assessed through a realistic copy detection and registration scenario.

The proliferation of pirate copies of feature films on peer-to-peer networks arouses a great interest to countermeasures such as the insertion of (invisible) forensic marks in projected movies, to deter their illegal capture. The registration of pirate copies with the original content is however a prerequisite to the recovery of such embedded messages, as severe geometric distortions often occur in illegally camcorded contents. After a brief state-of-the-art in image registration, the paper details an algorithm for video registration, focusing on the compensation of geometric distortions. Control points are automatically extracted in original and copy pictures, followed by pre- and post-matching filtering steps to discard not relevant control points and erroneous matched pairs of control points respectively. This enables the accurate numerical estimation of an 8-parameter homographic distortion model, used to register the copy frames with the original reference grid. Such an image registration algorithm is inserted into a general video registration scheme. Results are presented on both natural and synthetic test material.

Video frame alignment with the original source signal is required in a variety of applications, for instance non-blind watermark detection. Robustness to temporal cropping, scene removal, frame-rate changes and other non-linear temporal distortions are required. This paper presents an algorithm for locating a video segment within a long video document with frame-accurate alignment. The video sequences are first summarized into one-dimensional "temporal profile" signals. Then frame-to-frame correspondence is obtained by a dynamic programming approach. An automatic assessment of the matching result is proposed. Extensive test results achieved 77% success rate.

A fingerprinting design extracts discriminating features, called fingerprints. The extracted features are unique and specific to each image/video. The visual hash is usually a global fingerprinting technique with crypto-system constraints. In this paper, we propose an innovative video content identification process which combines a visual hash function and a local fingerprinting. Thanks to a visual hash function, we observe the video content variation and we detect key frames. A local image fingerprint technique characterizes the detected key frames. The set of local fingerprints for the whole video summarizes the video or fragments of the video. The video fingerprinting algorithm identifies an unknown video or a fragment of video within a video fingerprint database. It compares the local fingerprints of the candidate video with all local fingerprints of a database even if strong distortions are applied to an original content.

The tool presented in this paper performs an automatic segmentation of stereoscopic image sequences, based on the modeling of distance maps obtained by image processing. Two three-dimensional (3-D) image analysis algorithms are combined: (i) estimation of dense depth maps from stereoscopic image sequences and (ii) depth-based object segmentation. A Markovian statistical approach for such a segmentation of a dense depth map into arbitrarily shaped and oriented planar surfaces is described in detail. The simulation results on sequences "Fun fair" and "Tunnel", provided on video tape to the MPEG-4 tests of November 1995, are discussed.

This paper describes a region-based coding algorithm developed by Thomson Multimedia and submitted to MPEG-4 tests of November 1995 and January 1996. In this algorithm, the segmentation into regions is based on an advanced motion analysis, successively achieving a multiresolution motion field estimation and a segmentation based on a Markovian statistical approach, which insures a good temporal coherence of segmentation maps and an identification of covered areas. Moreover, the accurate motion description allows intermediate frames interpolation which can increase the displayed frame rate. Simulation results and MPEG-4 tests of January 1996 have shown that the algorithm is as efficient as block-based coding algorithms like MPEG-1 or H.263 for the compression functionality, while offering temporal scalability by higher frame rates. Moreover, description of the scene in coherent motion regions may be seen as an intermediate step toward object-based functionalities.