Online Learning and Experimentation via Interactive Learning Resources

Recent trends in online learning like Massive Open Online Courses (MOOCs) and Open Educational Resources (OERs) are changing the landscape in the education sector by allowing learners to self-regulate their learning and providing them with an abundant amount of free learning materials. This paper presents FORGE, a new European initiative for online learning and experimentation via interactive learning resources. FORGE provides learners and educators with access to world- class facilities and high quality learning materials, thus enabling them to carry out experiments on e.g. new Internet protocols. In turn, this supports constructivist and self-regulated learning approaches, through the use of interactive learning resources, such as eBooks

Doc. WG1m100113

Context and Objective: The current VM uses a dense 3D representation with dense convolutions Problem: Heavy in computation complexity, cannot encode a full point cloud at once; Underperforms for sparse point cloud Objective: Implement the DL models in the VM with a sparse tensor representation, and verify its performanceN/

IT/IST/IPLeiria Response to the Call for Evidence on JPEG Pleno Point Cloud Coding

This document proposes two scalable point cloud (PC) geometry codecs, submitted to the JPEG Call for Evidence on Point Cloud Coding (PCC).N/

Deep Learning-based Compressed Domain Multimedia for Man and Machine: A Taxonomy and Application to Point Cloud Classification

In the current golden age of multimedia, human visualization is no longer the single main target, with the final consumer often being a machine which performs some processing or computer vision tasks. In both cases, deep learning plays a undamental role in extracting features from the multimedia representation data, usually producing a compressed representation referred to as latent representation. The increasing development and adoption of deep learning-based solutions in a wide area of multimedia applications have opened an exciting new vision where a common compressed multimedia representation is used for both man and machine. The main benefits of this vision are two-fold: i) improved performance for the computer vision tasks, since the effects of coding artifacts are mitigated; and ii) reduced computational complexity, since prior decoding is not required. This paper proposes the first taxonomy for designing compressed domain computer vision solutions driven by the architecture and weights compatibility with an available spatio-temporal computer vision processor. The potential of the proposed taxonomy is demonstrated for the specific case of point cloud classification by designing novel compressed domain processors using the JPEG Pleno Point Cloud Coding standard under development and adaptations of the PointGrid classifier. Experimental results show that the designed compressed domain point cloud classification solutions can significantly outperform the spatial-temporal domain classification benchmarks when applied to the decompressed data, containing coding artifacts, and even surpass their performance when applied to the original uncompressed data

Deep Learning-Based Compressed Domain Multimedia for Man and Machine: A Taxonomy and Application to Point Cloud Classification

info:eu-repo/semantics/publishedVersio

Studying the Benefits of a New JPEG AI Profile for the JPEG PCC Verification Model: ISO/IEC JTC 1/SC29/WG1 M100115

The Joint Photographic Experts Group (JPEG) is a Working Group of ISO/IEC, the International Organisation for Standardization / International Electrotechnical Commission, (ISO/IEC JTC 1/SC 29/WG 1) and of the International Telecommunication Union (ITU-T SG16), responsible for the popular JPEG, JPEG 2000, JPEG XR, JPSearch, JPEG XT and more recently, the JPEG XS, JPEG Systems, JPEG Pleno, JPEG XL and JPEG AI families of imaging standards.JPEG AI: https://jpeg.org/jpegai/documentation.htmlContext and Objective: The current JPEG PCC VM color coding approach first projects the PC color onto 2D images, then uses JPEG AI to code the 2D images.N/

Doc. WG1m100090

The JPEG Pleno PCC scope is a learning-based PC coding standard offering a singlestream, compact, compressed domain representation, targeting both human visualization, with significant compression efficiency improvement over PC coding standards in common use at equivalent subjective quality, as well as effective performance for PC processing and computer vision tasks.N/

Doc. WG1m100108

Context and Objective: In the JPEG Pleno PC dataset, there are some PCs (e.g., sparse PCs) which are more ‘difficult’ to code and may benefit from improvements in the JPEG PCC VM DL coding model.N/

Neighborhood Adaptive Loss Function for Deep Learning-Based Point Cloud Coding With Implicit and Explicit Quantization

As the interest in deep learning tools continues to rise, new multimedia research fields begin to discover its potential. Both image and point cloud coding are good examples of technologies, where deep learning-based solutions have recently displayed very competitive performance. In this context, this article brings two novel contributions to the point cloud geometry coding state-of-the-art; first, a novel neighborhood adaptive distortion metric to be used in the training loss function, which allows significantly improving the rate-distortion performance with commonly used objective quality metrics; second, an explicit quantization approach at the training and coding times to generate varying rate/quality with a single trained deep learning coding model, effectively reducing the training complexity and storage requirements. The result is an improved deep learning-based point cloud geometry coding solution, which is both more compression efficient and less demanding in training complexity and storage.info:eu-repo/semantics/publishedVersio