The demonstration will present a real-time implementation of a platform jointly developed by InterDigital and Philips that showcases use cases leveraging the MPEG volumetric video codecs (MPEG-I V3C).

This article introduces the ISO/IEC MPEG Immersive Video (MIV) standard, MPEG-I Part 12, which is undergoing standardization. The draft MIV standard provides support for viewing immersive volumetric content captured by multiple cameras with six degrees of freedom (6DoF) within a viewing space that is determined by the camera arrangement in the capture rig. The bitstream format and decoding processes of the draft specification along with aspects of the Test Model for Immersive Video (TMIV) reference software encoder, decoder, and renderer are described. The use cases, test conditions, quality assessment methods, and experimental results are provided. In the TMIV, multiple texture and geometry views are coded as atlases of patches using a legacy 2-D video codec, while optimizing for bitrate, pixel rate, and quality. The design of the bitstream format and decoder is based on the visual volumetric video-based coding (V3C) and video-based point cloud compression (V-PCC) standard, MPEG-I Part 5.

New standards are emerging for the coding of volumetric 3D data such as immersive video and point clouds. Some of these volumetric encoders similarly utilize video codecs as the core of their compression approach, but apply different techniques to convert volumetric 3D data into 2D content for subsequent 2D video compression. Currently in MPEG there are two activities that follow this paradigm: ISO/IEC 23090-5 Video-based Point Cloud Compression (V-PCC) and ISO/IEC 23090-12 MPEG Immersive Video (MIV). In this article we propose for both standards to define 2D projection as common transmission format. We then describe a procedure based on camera projections that is applicable to both standards to convert 3D information into 2D images for efficient 2D compression. Results show that our approach successfully encodes both point clouds and immersive video content with the same performance as the current test models that MPEG experts developed separately for the respective standards. We conclude the article by discussing further integration steps and future directions.

In this paper we address the problem of eye localization in low and standard definition content, such as webcam-generated and TV images. We present a probabilistic eye localization method based on well-known multiscale local binary patterns (LBPs), which provide a simple and powerful spatial description of texture, and are robust to the noise typical to low and standard definition content. Our primary contribution is in the proposed method of combining the LBPs that is targeted towards achieving spatial accuracy under mentioned conditions. Evaluation performed on a standard dataset of webcam-quality images shows that our approach has superior performance with respect to the state of the art, while having a reasonable complexity and a low memory footprint.

This technical demo is build around a system we developed to automatically match persons within a video sequence. The persons are clustered by face similarity and shot information. The demo is a video editing tool with a "person query" option to show only that part of the sequence that features one of the selected actors. The demo is targeted for narrative content: series and movies, and shows the ability of the underlying person matching algorithm to operate well in a general (unconstrained) case.

The ever-increasing complexity of generic multimedia-content-analysis-based (MCA) solutions, their processing power demanding nature and the need to prototype and assess solutions in a fast and cost-saving manner motivated the development of the Cassandra framework. The combination of state-of-the-art network and grid-computing solutions and recently standardized interfaces facilitated the set-up of this framework, forming the basis for multiple cross-domain and cross-organizational collaborations. It enables distributed computing scenario simulations for e.g. distributed content analysis (DCA) across consumer electronics (CE) in-home networks, but also the rapid development and assessment of complex multi-MCA-algorithm-based applications and system solutions. Furthermore, the framework's modular nature-logical MCA units are wrapped into so-called service units (SU)-ease the split between system-architecture- and algorithmic-related work and additionally facilitate reusability, extensibility and upgrade ability of those SUs