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/

Contributions to HEVC Prediction for Medical Image Compression

Medical imaging technology and applications are continuously evolving, dealing with images of increasing spatial and temporal resolutions, which allow easier and more accurate medical diagnosis. However, this increase in resolution demands a growing amount of data to be stored and transmitted. Despite the high coding efficiency achieved by the most recent image and video coding standards in lossy compression, they are not well suited for quality-critical medical image compression where either near-lossless or lossless coding is required. In this dissertation, two different approaches to improve lossless coding of volumetric medical images, such as Magnetic Resonance and Computed Tomography, were studied and implemented using the latest standard High Efficiency Video Encoder (HEVC). In a first approach, the use of geometric transformations to perform inter-slice prediction was investigated. For the second approach, a pixel-wise prediction technique, based on Least-Squares prediction, that exploits inter-slice redundancy was proposed to extend the current HEVC lossless tools. Experimental results show a bitrate reduction between 45% and 49%, when compared with DICOM recommended encoders, and 13.7% when compared with standard HEVC

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

info:eu-repo/semantics/publishedVersio

Components of the leaf area index of marandu palisadegrass swards subjected to strategies of intermittent stocking

Leaf area index is the main sward characteristic related to the processes of light interception and competition in plant communities. The objective of this experiment was to quantify and evaluate the composition of the leaf area on tillers of marandu palisadegrass (Brachiaria brizantha cv. Marandu) subjected to strategies of intermittent stocking. The experiment was carried out in Piracicaba, state of São Paulo, Brazil, from October/2004 to December/2005. Swards were grazed at 95 and 100% canopy light interception (LI) to post-grazing heights of 10 and 15 cm, following a 2 ' 2 factorial arrangement with four replications in a randomised complete block design. Estimates were made of sward leaf area index, site filling, specific leaf area and the dimensionless ratio between tiller leaf area and volume (R), as well as the relative contribution of basal and aerial tillers to these variables. In early spring, values of leaf area index and specific leaf area were low when compared to the other seasons, and swards grazed at 95% LI presented higher site filling and specific leaf area than those grazed at 100% LI. This resulted in higher tillering activity and increase in leaf area index in late spring, indicating quick recovery and early return of swards grazed at 95% LI to growing conditions. Aerial tillers corresponded to an important morphological adaptation of marandu palisadegrass to increase its competitive ability. Treatment 100/10 resulted in the highest and 95/15 in the lowest R values throughout the experiment, suggesting an allometric pattern of growth of tillers during regrowth in order to compensate low tiller population and optimise the leaf area index. Grazing management practices can benefit from this knowledge by promoting ideal sward conditions to maximise and accelerate growth.O índice de área foliar é a principal característica do dossel relacionada com os processos de interceptação e competição por luz em comunidades de plantas. O objetivo deste experimento foi quantificar e avaliar a composição da área foliar dos perfilhos em pastos de capim-marandu (Brachiaria brizantha cv. Marandu) submetidos a estratégias de lotação intermitente. O experimento foi conduzido em Piracicaba, SP, Brasil, de outubro/2004 a dezembro/2005. Os pastejos foram realizados quando o dossel atingia 95 ou 100% de interceptação luminosa (IL) até as alturas pós-pastejo de 10 e 15 cm, seguindo um arranjo fatorial 2 ' 2 com 4 repetições e um delineamento de blocos completos casualizados. Foram estimados o índice de área foliar, site filling, área foliar específica e a relação adimensional entre área foliar e volume dos perfilhos (R), além da participação relativa de perfilhos basais e aéreos na composição dessas variáveis. No início de primavera os valores de índice de área foliar e área foliar específica foram baixos, e pastos manejados com 95% de IL apresentaram maior site filling e área foliar específica que pastos manejados com 100% de IL. Isso resultou em maior perfilhamento e aumento no índice de área foliar no final de primavera, indicando rápida recuperação e retorno precoce dos pastos manejados com 95% de IL a condições de crescimento. Os perfilhos aéreos corresponderam a uma importante adaptação morfológica do capim-marandu para aumentar sua habilidade competitiva. O tratamento 100/10 resultou nos maiores e o 95/15 nos menores valores de R, sugerindo um padrão alométrico de crescimento dos perfilhos durante a rebrotação para compensar baixa densidade populacional e otimizar o índice de área foliar. Práticas de manejo do pastejo podem se beneficiar desse conhecimento propiciando condições ideais de dossel para maximização e aceleração do crescimento

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/

Constant Size Point Cloud Clustering: a Compact, Non-Overlapping Solution

Point clouds have recently become a popular 3D representation model for many application domains, notably virtual and augmented reality. Since point cloud data is often very large, processing a point cloud may require that it be segmented into smaller clusters. For example, the input to deep learning-based methods like auto-encoders should be constant size point cloud clusters, which are ideally compact and non-overlapping. However, given the unorganized nature of point clouds, defining the specific data segments to code is not always trivial. This paper proposes a point cloud clustering algorithm which targets five main goals: i) clusters with a constant number of points; ii) compact clusters, i.e. with low dispersion; iii) non-overlapping clusters, i.e. not intersecting each other; iv) ability to scale with the number of points; and v) low complexity. After appropriate initialization, the proposed algorithm transfers points between neighboring clusters as a propagation wave, filling or emptying clusters until they achieve the same size. The proposed algorithm is unique since there is no other point cloud clustering method available in the literature offering the same clustering features for large point clouds at such low complexityinfo:eu-repo/semantics/acceptedVersio

Coding of Still Pictures

This document reports the performance results of the first version of the JPEG Pleno Point Cloud Coding Verification Model under Consideration, following the Call for Proposals on JPEG Pleno Point Cloud Coding issued in January 2022 [1].N/