8 research outputs found

    Dense light field coding: a survey

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    Light Field (LF) imaging is a promising solution for providing more immersive and closer to reality multimedia experiences to end-users with unprecedented creative freedom and flexibility for applications in different areas, such as virtual and augmented reality. Due to the recent technological advances in optics, sensor manufacturing and available transmission bandwidth, as well as the investment of many tech giants in this area, it is expected that soon many LF transmission systems will be available to both consumers and professionals. Recognizing this, novel standardization initiatives have recently emerged in both the Joint Photographic Experts Group (JPEG) and the Moving Picture Experts Group (MPEG), triggering the discussion on the deployment of LF coding solutions to efficiently handle the massive amount of data involved in such systems. Since then, the topic of LF content coding has become a booming research area, attracting the attention of many researchers worldwide. In this context, this paper provides a comprehensive survey of the most relevant LF coding solutions proposed in the literature, focusing on angularly dense LFs. Special attention is placed on a thorough description of the different LF coding methods and on the main concepts related to this relevant area. Moreover, comprehensive insights are presented into open research challenges and future research directions for LF coding.info:eu-repo/semantics/publishedVersio

    Compression and visual quality assessment for light field contents

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    Since its invention in the 19th century, photography has allowed to create durable images of the world around us by capturing the intensity of light that flows through a scene, first analogically by using light-sensitive material, and then, with the advent of electronic image sensors, digitally. However, one main limitation of both analog and digital photography lays in its inability to capture any information about the direction of light rays. Through traditional photography, each three-dimensional scene is projected onto a 2D plane; consequently, no information about the position of the 3D objects in space is retained. Light field photography aims at overcoming these limitations by recording the direction of light along with its intensity. In the past, several acquisition technologies have been presented to properly capture light field information, and portable devices have been commercialized to the general public. However, a considerably larger volume of data is generated when compared to traditional photography. Thus, new solutions must be designed to face the challenges light field photography poses in terms of storage, representation, and visualization of the acquired data. In particular, new and efficient compression algorithms are needed to sensibly reduce the amount of data that needs to be stored and transmitted, while maintaining an adequate level of perceptual quality. In designing new solutions to address the unique challenges posed by light field photography, one cannot forgo the importance of having reliable, reproducible means of evaluating their performance, especially in relation to the scenario in which they will be consumed. To that end, subjective assessment of visual quality is of paramount importance to evaluate the impact of compression, representation, and rendering models on user experience. Yet, the standardized methodologies that are commonly used to evaluate the visual quality of traditional media content, such as images and videos, are not equipped to tackle the challenges posed by light field photography. New subjective methodologies must be tailored for the new possibilities this new type of imaging offers in terms of rendering and visual experience. In this work, we address the aforementioned problems by both designing new methodologies for visual quality evaluation of light field contents, and outlining a new compression solution to efficiently reduce the amount of data that needs to be transmitted and stored. We first analyse how traditional methodologies for subjective evaluation of multimedia contents can be adapted to suit light field data, and, we propose new methodologies to reliably assess the visual quality while maintaining user engagement. Furthermore, we study how user behavior is affected by the visual quality of the data. We employ subjective quality assessment to compare several state-of-the-art solutions in light field coding, in order to find the most promising approaches to minimize the volume of data without compromising on the perceptual quality. To that means, we define and inspect several coding approaches for light field compression, and we investigate the impact of color subsampling on the final rendered content. Lastly, we propose a new coding approach to perform light field compression, showing significant improvement with respect to the state of the art

    Reconstruction from Spatio-Spectrally Coded Multispectral Light Fields

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    In dieser Arbeit werden spektral kodierte multispektrale Lichtfelder untersucht, wie sie von einer Lichtfeldkamera mit einem spektral kodierten Mikrolinsenarray aufgenommen werden. Für die Rekonstruktion der kodierten Lichtfelder werden zwei Methoden entwickelt, eine basierend auf den Prinzipien des Compressed Sensing sowie eine Deep Learning Methode. Anhand neuartiger synthetischer und realer Datensätze werden die vorgeschlagenen Rekonstruktionsansätze im Detail evaluiert

    Reconstruction from Spatio-Spectrally Coded Multispectral Light Fields

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    In this work, spatio-spectrally coded multispectral light fields, as taken by a light field camera with a spectrally coded microlens array, are investigated. For the reconstruction of the coded light fields, two methods, one based on the principles of compressed sensing and one deep learning approach, are developed. Using novel synthetic as well as a real-world datasets, the proposed reconstruction approaches are evaluated in detail

    Reconstruction from Spatio-Spectrally Coded Multispectral Light Fields

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    In dieser Arbeit werden spektral codierte multispektrale Lichtfelder, wie sie von einer Lichtfeldkamera mit einem spektral codierten Mikrolinsenarray aufgenommen werden, untersucht. Für die Rekonstruktion der codierten Lichtfelder werden zwei Methoden entwickelt und im Detail ausgewertet. Zunächst wird eine vollständige Rekonstruktion des spektralen Lichtfelds entwickelt, die auf den Prinzipien des Compressed Sensing basiert. Um die spektralen Lichtfelder spärlich darzustellen, werden 5D-DCT-Basen sowie ein Ansatz zum Lernen eines Dictionary untersucht. Der konventionelle vektorisierte Dictionary-Lernansatz wird auf eine tensorielle Notation verallgemeinert, um das Lichtfeld-Dictionary tensoriell zu faktorisieren. Aufgrund der reduzierten Anzahl von zu lernenden Parametern ermöglicht dieser Ansatz größere effektive Atomgrößen. Zweitens wird eine auf Deep Learning basierende Rekonstruktion der spektralen Zentralansicht und der zugehörigen Disparitätskarte aus dem codierten Lichtfeld entwickelt. Dabei wird die gewünschte Information direkt aus den codierten Messungen geschätzt. Es werden verschiedene Strategien des entsprechenden Multi-Task-Trainings verglichen. Um die Qualität der Rekonstruktion weiter zu verbessern, wird eine neuartige Methode zur Einbeziehung von Hilfslossfunktionen auf der Grundlage ihrer jeweiligen normalisierten Gradientenähnlichkeit entwickelt und gezeigt, dass sie bisherige adaptive Methoden übertrifft. Um die verschiedenen Rekonstruktionsansätze zu trainieren und zu bewerten, werden zwei Datensätze erstellt. Zunächst wird ein großer synthetischer spektraler Lichtfelddatensatz mit verfügbarer Disparität Ground Truth unter Verwendung eines Raytracers erstellt. Dieser Datensatz, der etwa 100k spektrale Lichtfelder mit dazugehöriger Disparität enthält, wird in einen Trainings-, Validierungs- und Testdatensatz aufgeteilt. Um die Qualität weiter zu bewerten, werden sieben handgefertigte Szenen, so genannte Datensatz-Challenges, erstellt. Schließlich wird ein realer spektraler Lichtfelddatensatz mit einer speziell angefertigten spektralen Lichtfeldreferenzkamera aufgenommen. Die radiometrische und geometrische Kalibrierung der Kamera wird im Detail besprochen. Anhand der neuen Datensätze werden die vorgeschlagenen Rekonstruktionsansätze im Detail bewertet. Es werden verschiedene Codierungsmasken untersucht -- zufällige, reguläre, sowie Ende-zu-Ende optimierte Codierungsmasken, die mit einer neuartigen differenzierbaren fraktalen Generierung erzeugt werden. Darüber hinaus werden weitere Untersuchungen durchgeführt, zum Beispiel bezüglich der Abhängigkeit von Rauschen, der Winkelauflösung oder Tiefe. Insgesamt sind die Ergebnisse überzeugend und zeigen eine hohe Rekonstruktionsqualität. Die Deep-Learning-basierte Rekonstruktion, insbesondere wenn sie mit adaptiven Multitasking- und Hilfslossstrategien trainiert wird, übertrifft die Compressed-Sensing-basierte Rekonstruktion mit anschließender Disparitätsschätzung nach dem Stand der Technik

    Reconstruction from Spatio-Spectrally Coded Multispectral Light Fields

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    In this work, spatio-spectrally coded multispectral light fields, as taken by a light field camera with a spectrally coded microlens array, are investigated. For the reconstruction of the coded light fields, two methods, one based on the principles of compressed sensing and one deep learning approach, are developed. Using novel synthetic as well as a real-world datasets, the proposed reconstruction approaches are evaluated in detail

    Novi algoritam za kompresiju seizmičkih podataka velike amplitudske rezolucije

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    Renewable sources cannot meet energy demand of a growing global market. Therefore, it is expected that oil & gas will remain a substantial sources of energy in a coming years. To find a new oil & gas deposits that would satisfy growing global energy demands, significant efforts are constantly involved in finding ways to increase efficiency of a seismic surveys. It is commonly considered that, in an initial phase of exploration and production of a new fields, high-resolution and high-quality images of the subsurface are of the great importance. As one part in the seismic data processing chain, efficient managing and delivering of a large data sets, that are vastly produced by the industry during seismic surveys, becomes extremely important in order to facilitate further seismic data processing and interpretation. In this respect, efficiency to a large extent relies on the efficiency of the compression scheme, which is often required to enable faster transfer and access to data, as well as efficient data storage. Motivated by the superior performance of High Efficiency Video Coding (HEVC), and driven by the rapid growth in data volume produced by seismic surveys, this work explores a 32 bits per pixel (b/p) extension of the HEVC codec for compression of seismic data. It is proposed to reassemble seismic slices in a format that corresponds to video signal and benefit from the coding gain achieved by HEVC inter mode, besides the possible advantages of the (still image) HEVC intra mode. To this end, this work modifies almost all components of the original HEVC codec to cater for high bit-depth coding of seismic data: Lagrange multiplier used in optimization of the coding parameters has been adapted to the new data statistics, core transform and quantization have been reimplemented to handle the increased bit-depth range, and modified adaptive binary arithmetic coder has been employed for efficient entropy coding. In addition, optimized block selection, reduced intra prediction modes, and flexible motion estimation are tested to adapt to the structure of seismic data. Even though the new codec after implementation of the proposed modifications goes beyond the standardized HEVC, it still maintains a generic HEVC structure, and it is developed under the general HEVC framework. There is no similar work in the field of the seismic data compression that uses the HEVC as a base codec setting. Thus, a specific codec design has been tailored which, when compared to the JPEG-XR and commercial wavelet-based codec, significantly improves the peak-signal-tonoise- ratio (PSNR) vs. compression ratio performance for 32 b/p seismic data. Depending on a proposed configurations, PSNR gain goes from 3.39 dB up to 9.48 dB. Also, relying on the specific characteristics of seismic data, an optimized encoder is proposed in this work. It reduces encoding time by 67.17% for All-I configuration on trace image dataset, and 67.39% for All-I, 97.96% for P2-configuration and 98.64% for B-configuration on 3D wavefield dataset, with negligible coding performance losses. As a side contribution of this work, HEVC is analyzed within all of its functional units, so that the presented work itself can serve as a specific overview of methods incorporated into the standard
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