4,483 research outputs found
Light Field Compression by Residual CNN Assisted JPEG
Light field (LF) imaging has gained significant attention due to its recent
success in 3-dimensional (3D) displaying and rendering as well as augmented and
virtual reality usage. Nonetheless, because of the two extra dimensions, LFs
are much larger than conventional images. We develop a JPEG-assisted
learning-based technique to reconstruct an LF from a JPEG bitstream with a bit
per pixel ratio of 0.0047 on average. For compression, we keep the LF's center
view and use JPEG compression with 50% quality. Our reconstruction pipeline
consists of a small JPEG enhancement network (JPEG-Hance), a depth estimation
network (Depth-Net), followed by view synthesizing by warping the enhanced
center view. Our pipeline is significantly faster than using video compression
on pseudo-sequences extracted from an LF, both in compression and
decompression, while maintaining effective performance. We show that with a 1%
compression time cost and 18x speedup for decompression, our methods
reconstructed LFs have better structural similarity index metric (SSIM) and
comparable peak signal-to-noise ratio (PSNR) compared to the state-of-the-art
video compression techniques used to compress LFs
Light field coding with field of view scalability and exemplar-based inter-layer prediction
Light field imaging based on microlens arrays—a.k.a. holoscopic, plenoptic, and integral imaging—has currently risen up as a feasible and prospective technology for future image and video applications. However, deploying actual light field applications will require identifying more powerful representations and coding solutions that support arising new manipulation and interaction functionalities. In this context, this paper proposes a novel scalable coding solution that supports a new type of scalability, referred to as field-of-view scalability. The proposed scalable coding solution comprises a base layer compliant with the High Efficiency Video Coding (HEVC) standard, complemented by one or more enhancement layers that progressively allow richer versions of the same light field content in terms of content manipulation and interaction possibilities. In addition, to achieve high-compression performance in the enhancement layers, novel exemplar-based interlayer coding tools are also
proposed, namely: 1) a direct prediction based on exemplar texture samples from lower layers and 2) an interlayer compensated prediction using a reference picture that is built relying on an exemplar-based algorithm for texture synthesis. Experimental results demonstrate the advantages of the proposed scalable coding solution to cater to users with different preferences/requirements in terms of interaction functionalities, while providing better rate-
distortion performance (independently of the optical setup used for acquisition) compared to HEVC and other scalable light field coding solutions in the literature.info:eu-repo/semantics/acceptedVersio
From Capture to Display: A Survey on Volumetric Video
Volumetric video, which offers immersive viewing experiences, is gaining
increasing prominence. With its six degrees of freedom, it provides viewers
with greater immersion and interactivity compared to traditional videos.
Despite their potential, volumetric video services poses significant
challenges. This survey conducts a comprehensive review of the existing
literature on volumetric video. We firstly provide a general framework of
volumetric video services, followed by a discussion on prerequisites for
volumetric video, encompassing representations, open datasets, and quality
assessment metrics. Then we delve into the current methodologies for each stage
of the volumetric video service pipeline, detailing capturing, compression,
transmission, rendering, and display techniques. Lastly, we explore various
applications enabled by this pioneering technology and we present an array of
research challenges and opportunities in the domain of volumetric video
services. This survey aspires to provide a holistic understanding of this
burgeoning field and shed light on potential future research trajectories,
aiming to bring the vision of volumetric video to fruition.Comment: Submitte
Light field image coding with flexible viewpoint scalability and random access
This paper proposes a novel light field image compression approach with viewpoint scalability and random access functionalities. Although current state-of-the-art image coding algorithms for light fields already achieve high compression ratios, there is a lack of support for such functionalities, which are important for ensuring compatibility with different displays/capturing devices, enhanced user interaction and low decoding delay. The proposed solution enables various encoding profiles with different flexible viewpoint scalability and random access capabilities, depending on the application scenario. When compared to other state-of-the-art methods, the proposed approach consistently presents higher bitrate savings (44% on average), namely when compared to pseudo-video sequence coding approach based on HEVC. Moreover, the proposed scalable codec also outperforms MuLE and WaSP verification models, achieving average bitrate saving gains of 37% and 47%, respectively. The various flexible encoding profiles proposed add fine control to the image prediction dependencies, which allow to exploit the tradeoff between coding efficiency and the viewpoint random access, consequently, decreasing the maximum random access penalties that range from 0.60 to 0.15, for lenslet and HDCA light fields.info:eu-repo/semantics/acceptedVersio
Capture4VR: From VR Photography to VR Video
Virtual reality (VR) enables the display of dynamic visual content with unparalleled realism and immersion. However, VR is also still a relatively young medium that requires new ways to author content, particularly for visual content that is captured from the real world. This course, therefore, provides a comprehensive overview of the latest progress in bringing photographs and video into VR. Ultimately, the techniques, approaches and systems we discuss aim to faithfully capture the visual appearance and dynamics of the real world, and to bring it into virtual reality to create unparalleled realism and immersion by providing freedom of head motion and motion parallax, which is a vital depth cue for the human visual system. In this half-day course, we take the audience on a journey from VR photography to VR video that began more than a century ago but which has accelerated tremendously in the last five years. We discuss both commercial state-of-the-art systems by Facebook, Google and Microsoft, as well as the latest research techniques and prototypes
Light field image processing: an overview
Light field imaging has emerged as a technology allowing to capture richer visual information from our world. As opposed to traditional photography, which captures a 2D projection of the light in the scene integrating the angular domain, light fields collect radiance from rays in all directions, demultiplexing the angular information lost in conventional photography. On the one hand, this higher dimensional representation of visual data offers powerful capabilities for scene understanding, and substantially improves the performance of traditional computer vision problems such as depth sensing, post-capture refocusing, segmentation, video stabilization, material classification, etc. On the other hand, the high-dimensionality of light fields also brings up new challenges in terms of data capture, data compression, content editing, and display. Taking these two elements together, research in light field image processing has become increasingly popular in the computer vision, computer graphics, and signal processing communities. In this paper, we present a comprehensive overview and discussion of research in this field over the past 20 years. We focus on all aspects of light field image processing, including basic light field representation and theory, acquisition, super-resolution, depth estimation, compression, editing, processing algorithms for light field display, and computer vision applications of light field data
- …