Percorrer por autor "Seleem, Abdelrahman"
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- Adaptive bridge model for compressed domain point cloud classificationPublication . Seleem, Abdelrahman; Guarda, André F. R.; Rodrigues, Nuno M. M.; Pereira, FernandoThe recent adoption of deep learning-based models for the processing and coding of multimedia signals has brought noticeable gains in performance, which have established deep learning-based solutions as the uncontested state-of-the-art both for computer vision tasks, targeting machine consumption, as well as, more recently, coding applications, targeting human visualization. Traditionally, applications requiring both coding and computer vision processing require frst decoding the bitstream and then applying the computer vision methods to the decompressed multimedia signals. However, the adoption of deep learning-based solutions enables the use of compressed domain computer vision processing, with gains in performance and computational complexity over the decompressed domain approach. For point clouds (PCs), these gains have been demonstrated in the single available compressed domain computer vision processing solution, named Compressed Domain PC Classifer, which processes JPEG Pleno PC coding (PCC) compressed streams using a PC classifer largely compatible with the state-of-the-art spatial domain PointGrid classifer. However, the available Compressed Domain PC Classifer presents strong limitations by imposing a single, specifc input size which is associated to specifc JPEG Pleno PCC confgurations; this limits the compression performance as these confgurations are not ideal for all PCs due to their diferent characteristics, notably density. To overcome these limitations, this paper proposes the frst Adaptive Compressed Domain PC Classifer solution which includes a novel adaptive bridge model that allows to process the JPEG Pleno PCC encoded bit streams using diferent coding confgurations, now maximizing the compression efciency. Experimental results show that the novel Adaptive Compressed Domain PC Classifer allows JPEG PCC to achieve better compression performance by not imposing a single, specifc coding confguration for all PCs, regardless of its diferent characteristics. Moreover, the added adaptability power can achieve slightly better PC classifcation performance than the previous Compressed Domain PC Classifer and largely better PC classifcation performance (and lower number of weights) than the PointGrid PC classifer working in the decompressed domain.
- Deep Learning-Based Compressed Domain Multimedia for Man and Machine: A Taxonomy and Application to Point Cloud ClassificationPublication . Seleem, Abdelrahman; Guarda, André F. R.; M. M. Rodrigues, Nuno; Pereira, Fernando
- Deep Learning-based Compressed Domain Point Cloud ClassificationPublication . Seleem, Abdelrahman; Guarda, André; Rodrigues, Nuno; Pereira, FernandoThe 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.
- Deep Learning-Based Point Cloud Coding and Super-Resolution: a Joint Geometry and Color ApproachPublication . Guarda, André F. R.; Ruivo, Manuel; Coelho, Luís; Seleem, Abdelrahman; M. M. Rodrigues, Nuno; Pereira, FernandoIn this golden age of multimedia, realistic content is in high demand with users seeking more immersive and interactive experiences. As a result, new image modalities for 3D representations have emerged in recent years, among which point clouds have deserved especial attention. Naturally, with this increase in demand, efficient storage and transmission became a must, with standardization groups such as MPEG and JPEG entering the scene, as it happened before with other types of visual media. In a surprising development, JPEG issued a Call for Proposals on point cloud coding targeting exclusively learningbased solutions, in parallel to a similar call for image coding. This is a natural consequence of the growing popularity of deep learning, which due to its excellent performances is currently dominant in the multimedia processing field, including coding. This paper presents the coding solution selected by JPEG as the best-performing response to the Call for Proposals and adopted as the first version of the JPEG Pleno Point Cloud Coding Verification Model, in practice the first step for developing a standard. The proposed solution offers a novel joint geometry and color approach for point cloud coding, in which a single deep learning model processes both geometry and color simultaneously. To maximize the RD performance for a large range of point clouds, the proposed solution uses down-sampling and learningbased super-resolution as pre- and post-processing steps. Compared to the MPEG point cloud coding standards, the proposed coding solution comfortably outperforms G-PCC, for both geometry, color, and joint quality metrics.
- A Double Deep Learning-Based Solution for Efficient Event Data Coding and ClassificationPublication . Seleem, Abdelrahman; Guarda, André; M. M. Rodrigues, Nuno; Pereira, FernandoEvent cameras have the ability to capture asynchronous per-pixel brightness changes, usually called "events", offering advantages over traditional frame-based cameras for computer vision tasks. Efficiently coding event data is critical for practical transmission and storage, given the very significant number of events captured. This paper proposes a novel double deep learning-based solution for efficient event data coding and classification, using a point cloud-based representation for events. Moreover, since the conversions from events to point clouds and back to events are key steps in the proposed solution, novel tools are proposed and their impact is evaluated in terms of compression and classification performance. Experimental results show that it is possible to achieve a classification performance for decompressed events which is similar to the one for original events, even after applying a lossy point cloud codec, notably the recent deep learning-based JPEG Pleno Point Cloud Coding standard, with a clear rate reduction. Experimental results also demonstrate that events coded using the JPEG standard achieve better classification performance than those coded using the conventional lossy MPEG Geometry-based Point Cloud Coding standard for the same rate. Furthermore, the adoption of deep learning-based coding offers future high potential for performing computer vision tasks in the compressed domain, which allows skipping the decoding stage, thus mitigating the impact of compression artifact
- IT/IST/IPLeiria Response to the Call for Proposals on JPEG Pleno Point Cloud CodingPublication . Guarda, André F. R.; Rodrigues, Nuno M. M.; Ruivo, Manuel; Coelho, Luís; Seleem, Abdelrahman; Pereira, FernandoThis document describes a deep learning (DL)-based point cloud (PC) geometry codec and a DL-based PC joint geometry and colour codec, submitted to the Call for Proposals on JPEG Pleno Point Cloud Coding issued in January 2022 [1]. These proposals have been originated by research developed at Instituto de Telecomunicações (IT), in the context of the project Deep-PCR entitled “Deep learning-based Point Cloud Representation” (PTDC/EEI-COM/1125/2021), financed by Fundação para a Ciência e Tecnologia (FCT).
- JPEG Pleno Point Cloud Coding Verification Model DescriptionPublication . Guarda, André F. R.; Rodrigues, Nuno M. M.; Ruivo, Manuel; Coelho, Luís; Seleem, Abdelrahman; Pereira, FernandoThis document describes the JPEG Pleno Point Cloud Coding [1] Verification Model (VM), consisting of a deep learning (DL)-based joint point cloud (PC) geometry and colour codec [2].
- Studying the Benefits of a New JPEG AI Profile for the JPEG PCC Verification Model: ISO/IEC JTC 1/SC29/WG1 M100115Publication . Seleem, Abdelrahman; Guarda, André; Rodrigues, Nuno; Pereira, FernandoContext 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.