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Dong Liu - One of the best experts on this subject based on the ideXlab platform.

  • Quadtree-Based Coding Framework for High-Density Camera Array-Based Light Field Image
    IEEE Transactions on Circuits and Systems for Video Technology, 2020
    Co-Authors: Dong Liu
    Abstract:

    The size of a high-density-camera-array (HDCA)-based light Field Image (LFI) is usually very large, containing hundreds of high-resolution views. Therefore, there is an urgent need to efficiently compress it. Currently, no compression algorithms, specially, for the HDCA-based LFI have been designed. In this paper, we propose an algorithm based on a quadtree-based 2D hierarchical coding framework for the HDCA-based LFI data compression. The proposed framework has the following contributions. First, we organize the views of the HDCA-based LFI into a quadtree-based coding structure. Under this structure, all of the views are divided into four quadrants at the first level. Each quadrant is further sub-divided into four quadrants at each subsequent level. The process continues until the desired depth is reached. This quadtree-based coding structure can make full use of the strong inter-view correlations to improve the coding efficiency. In addition, the proposed quadtree-based structure can be easily extended to a general 2D hierarchical structure with variable group of pictures (GOP) sizes to adapt to the reference frame buffer constraint. Second, we try to improve the performance of the 2D hierarchical coding framework using the distance-based criteria for both the reference frame selection and motion vector scaling. Third, a one-pass optimal bit allocation scheme is proposed to further optimize the performance by taking the quality dependencies among various views into consideration. The proposed framework is implemented in the newest video coding standard, high efficiency video coding (HEVC). The experimental results show that the proposed quadtree-based 2D hierarchical coding framework can achieve an average of over 25% bitrate saving compared with the 1D hierarchical coding structure.

  • pseudo sequence based 2 d hierarchical coding structure for light Field Image compression
    IEEE Journal of Selected Topics in Signal Processing, 2017
    Co-Authors: Dong Liu
    Abstract:

    In this paper, we propose a pseudo-sequence-based two-dimensional (2-D) hierarchical coding structure for light-Field Image compression. In the proposed scheme, we first decompose the light-Field Image into multiple views and organize them into a 2-D coding structure according to the spatial coordinates of the corresponding microlens. Then, we mainly develop three algorithms to optimize the 2-D coding structure. First, we propose a 2-D hierarchical coding structure with a limited number of reference frames to exploit the intercorrelations among various views. To be more specific, we divide all the views into four quadrants, and all the views are encoded one quadrant after another to reduce the reference buffer size as much as possible. Inside each quadrant, all the views are encoded hierarchically in both horizontal and vertical directions to fully exploit the correlations among different views. Second, we propose to use the distance between the current view and its reference views instead of the picture order count difference as the criterion for selecting better reference frames for each inter view. The distance-based criterion is also applied to the motion vector scaling process to obtain more accurate motion vector predictors. Third, an optimal bit allocation algorithm taking the influence of the various views on the following encoding views into account is proposed to further exploit the intercorrelations among various views and improve coding efficiency. The entire scheme is implemented in the reference software of high efficiency video coding. The experimental results demonstrate that the proposed novel pseudo-sequence-based 2-D hierarchical structure can achieve maximum 28.4% bit-rate savings compared with the previous pseudo-sequence-based light-Field Image compression method.

  • pseudo sequence based 2 d hierarchical coding structure for light Field Image compression
    Data Compression Conference, 2017
    Co-Authors: Dong Liu
    Abstract:

    In this paper, we present a novel pseudo sequence based 2-D hierarchical reference structure for light-Field Image compression. In the proposed scheme, we first decompose the light-Field Image into multiple views and organize them into a 2-D coding structure according to the spatial coordinates of the corresponding microlens. Then we mainly develop three technologies to optimize the 2-D coding structure. First, we divide all the views into four quadrants, and all the views are encoded one quadrant after another to reduce the reference buffer size as much as possible. Inside each quadrant, all the views are encoded hierarchically to fully exploit the correlations between different views. Second, we propose to use the distance between the current view and its reference views as the criteria for selecting better reference frames for each inter view. Third, we propose to use the spatial relative positions between different views to achieve more accurate motion vector scaling. The whole scheme is implemented in the reference software of High Efficiency Video Coding. The experimental results demonstrate that the proposed novel pseudo-sequence based 2-D hierarchical structure can achieve maximum 14.2% bit-rate savings compared with the state-of-the-art light-Field Image compression method.

  • Pseudo-sequence-based light Field Image compression
    2016 IEEE International Conference on Multimedia and Expo Workshop ICMEW 2016, 2016
    Co-Authors: Dong Liu, Xiong Zhiwei, Feng Wu, Lei Li, Lizhi Wang, Zeng Wenjun
    Abstract:

    We propose a pseudo-sequence-based scheme for light Field Image compression. In our scheme, the raw Image captured by a light Field camera is decomposed into multiple views according to the lenslet array of that camera. These views constitute a pseudo sequence like video, and the redundancy between views is exploited by a video encoder. The specific coding order of views, prediction structure, and rate allocation have been investigated for encoding the pseudo sequence. Experimental results show the superior performance of our scheme, which achieves as high as 6.6 dB gain compared with directly encoding the raw Image by the legacy JPEG.

Touradj Ebrahimi - One of the best experts on this subject based on the ideXlab platform.

  • quality assessment of compression solutions for icip 2017 grand challenge on light Field Image coding
    International Conference on Multimedia and Expo, 2018
    Co-Authors: Irene Viola, Touradj Ebrahimi
    Abstract:

    In recent years, the research community has witnessed a growing interest in immersive representations of the real world, such as light Field. However, due to the increased volume of data generated in the acquisition, new and efficient compression algorithms are needed to store and deliver light Field contents. A Grand Challenge on light Field Image coding was organised during ICIP 2017 to collect and evaluate new compression algorithms for lenslet-based light Field Images. This paper reports the results of the objective and subjective evaluation campaign conducted to assess the responses to the grand challenge. An adjectival categorical rating methodology with 7-point grading scale was selected to perform subjective assessments, whereas the objective assessment was conducted using popular Image quality metrics. Results show that two proposals have comparable performance and outperform the others across all bitrates.

  • Comparison and Evaluation of Light Field Image Coding Approaches
    IEEE Journal of Selected Topics in Signal Processing, 2017
    Co-Authors: Irene Viola, Martin Rerabek, Touradj Ebrahimi
    Abstract:

    The recent advances in light Field imaging, supported among others by the introduction of commercially available cameras, e.g., Lytro or Raytrix, are changing the ways in which visual content is captured and processed. Efficient storage and delivery systems for light Field Images must rely on compression algorithms. Several methods to compress light Field Images have been proposed recently. However, in-depth evaluations of compression algorithms have rarely been reported. This paper aims at the evaluation of perceived visual quality of light Field Images and at comparing the performance of a few state-of-the-art algorithms for light Field Image compression. First, a processing chain for light Field Image compression and decompression is defined for two typical use cases, professional and consumer. Then, five light Field compression algorithms are compared by means of a set of objective and subjective quality assessments. An interactive methodology recently introduced by authors, as well as a passive methodology is used to perform these evaluations. The results provide a useful benchmark for future development of compression solutions for light Field Images.

  • New Light Field Image Dataset
    8th International Conference on Quality of Multimedia Experience (QoMEX), 2016
    Co-Authors: Martin Rerabek, Touradj Ebrahimi
    Abstract:

    Recently, an emerging light Field imaging technol- ogy, which enables capturing full light information in a scene, has gained a lot of interest. To design, develop, implement, and test novel algorithms in light Field Image processing and compression, the availability of suitable light Field Image datasets is essential. In this paper, a publicly available light Field Image dataset is introduced and described in details. The proposed dataset contains 118 light Field Images captured by using a Lytro Illum light Field camera. Based on their content, acquired light Field Images were classified into ten different categories with various features covering wide range of potential usage, such as Image compression and quality evaluation.

  • objective and subjective evaluation of light Field Image compression algorithms
    Picture Coding Symposium, 2016
    Co-Authors: Irene Viola, Martin Rerabek, Tim Bruylants, Peter Schelkens, Fernando Pereira, Touradj Ebrahimi
    Abstract:

    This paper reports results of subjective and objective quality assessments of responses to a grand challenge on light Field Image compression. The goal of the challenge was to collect and evaluate new compression algorithms for light Field Images. In total seven proposals were received, out of which five were accepted for further evaluations. For objective evaluations, conventional metrics were used, whereas the double stimulus continuous quality scale method was selected to perform subjective assessments. Results show competitive performance among submitted proposals. However, in low bitrates, one proposal outperforms the others.

In So Kweon - One of the best experts on this subject based on the ideXlab platform.

  • light Field Image super resolution using convolutional neural network
    IEEE Signal Processing Letters, 2017
    Co-Authors: Youngjin Yoon, Haegon Jeon, Donggeun Yoo, Joonyoung Lee, In So Kweon
    Abstract:

    Commercial light Field cameras provide spatial and angular information, but their limited resolution becomes an important problem in practical use. In this letter, we present a novel method for light Field Image super-resolution (SR) to simultaneously up-sample both the spatial and angular resolutions of a light Field Image via a deep convolutional neural network. We first augment the spatial resolution of each subaperture Image by a spatial SR network, then novel views between super-resolved subaperture Images are generated by three different angular SR networks according to the novel view locations. We improve both the efficiency of training and the quality of angular SR results by using weight sharing . In addition, we provide a new light Field Image dataset for training and validating the network. We train our whole network end-to-end, and show state-of-the-art performances on quantitative and qualitative evaluations.

  • learning a deep convolutional network for light Field Image super resolution
    International Conference on Computer Vision, 2015
    Co-Authors: Youngjin Yoon, Haegon Jeon, Donggeun Yoo, Joonyoung Lee, In So Kweon
    Abstract:

    Commercial Light-Field cameras provide spatial and angular information, but its limited resolution becomes an important problem in practical use. In this paper, we present a novel method for Light-Field Image super-resolution (SR) via a deep convolutional neural network. Rather than the conventional optimization framework, we adopt a datadriven learning method to simultaneously up-sample the angular resolution as well as the spatial resolution of a Light-Field Image. We first augment the spatial resolution of each sub-aperture Image to enhance details by a spatial SR network. Then, novel views between the sub-aperture Images are generated by an angular super-resolution network. These networks are trained independently but finally finetuned via end-to-end training. The proposed method shows the state-of-the-art performance on HCI synthetic dataset, and is further evaluated by challenging real-world applications including refocusing and depth map estimation.

Ulf Jennehag - One of the best experts on this subject based on the ideXlab platform.

  • Handbook of Dynamic Data Driven Applications Systems - Light Field Image Compression
    3D Visual Content Creation Coding and Delivery, 2018
    Co-Authors: Caroline Conti, Mårten Sjöström, Roger Olsson, Paulo Nunes, Luis Ducla Soares, Cristian Perra, Pedro Assuncao, Ulf Jennehag
    Abstract:

    Light Field imaging based on a single-tier camera equipped with a micro-lens array has currently risen up as a practical and prospective approach for future visual applications and services. However, successfully deploying actual light Field imaging applications and services will require identifying adequate coding solutions to efficiently handle the massive amount of data involved in these systems. In this context, this chapter presents some of the most recent light Field Image coding solutions that have been investigated. After a brief review of the current state of the art in Image coding formats for light Field photography, an experimental study of the rate-distortion performance for different coding formats and architectures is presented. Then, aiming at enabling faster deployment of light Field applications and services in the consumer market, a scalable light Field coding solution that provides backward compatibility with legacy display devices (e.g., 2D, 3D stereo, and 3D multiview) is also presented. Furthermore, a light Field coding scheme based on a sparse set of microImages and the associated blockwise disparity is also presented. This coding scheme is scalable with three layers such that the rendering can be performed with the sparse micro-Image set, the reconstructed light Field Image, and the decoded light Field Image.

  • Efficient intra prediction scheme for light Field Image compression
    ICASSP IEEE International Conference on Acoustics Speech and Signal Processing - Proceedings, 2014
    Co-Authors: Yun Li, Mårten Sjöström, Roger Olsson, Ulf Jennehag
    Abstract:

    Interactive photo-realistic graphics can be rendered by using light Field datasets. One way of capturing the dataset is by using light Field cameras with microlens arrays. The captured Images contain repetitive patterns resulted from adjacent mi-crolenses. These Images don't resemble the appearance of a natural scene. This dissimilarity leads to problems in light Field Image compression by using traditional Image and video encoders, which are optimized for natural Images and video sequences. In this paper, we introduce the full inter-prediction scheme in HEVC into intra-prediction for the compression of light Field Images. The proposed scheme is capable of performing both unidirectional and bi-directional prediction within an Image. The evaluation results show that above 3 dB quality improvements or above 50 percent bit-rate saving can be achieved in terms of BD-PSNR for the proposed scheme compared to the original HEVC intra-prediction for light Field Images.

Zhang Xiong - One of the best experts on this subject based on the ideXlab platform.

  • geometric occlusion analysis in depth estimation using integral guided filter for light Field Image
    IEEE Transactions on Image Processing, 2017
    Co-Authors: Hao Sheng, Shuo Zhang, Xiaochun Cao, Yajun Fang, Zhang Xiong
    Abstract:

    Unlike traditional multi-view Images, sampling in angular domain of light Field Images is distributed in different directions. Therefore, an angular sampling Image (ASI), comprising of possible matching points extracted from each view, is available for each point. In this paper, we analyze the geometric relationship between ASIs and reference sub-aperture Images, and then prove the occlusion boundary similarity. Based on the geometric relationship in extreme cases, we show that some points in ASI have higher reliability than other points for depth calculation. An integral guided filter is then built based on the sub-aperture Image to predict occlusion probabilities in ASIs. The filter is independent of ASIs and has no requirement for high angular resolution so that it is easy to apply to the cost volume calculation. We integrate the filter into our depth estimation framework and other state-of-the-art depth estimation frameworks. Experimental results demonstrate that the proposed filter is more effective to occluded point detection in ASIs than other methods. Results from different data sets show that our method outperforms the existing state-of-the-art depth estimation methods, especially along occlusion boundaries.

  • ICIP - Segmentation of light Field Image with the structure tensor
    2016 IEEE International Conference on Image Processing (ICIP), 2016
    Co-Authors: Hao Sheng, Senyou Deng, Shuo Zhang, Zhang Xiong
    Abstract:

    We propose a segmentation model for light Field Images based on superpixels segmentation and graph-cuts algorithm. Unlike traditional Images, which do not offer information for different directions, a light Field Image encodes space data which can be computed on its epipolar plane Images (EPI) with some effective methods. In our work, we analyze the structure of EPI and research the computational process of disparity using EPI. On this basis, we present a new method for computing disparity using the modified structure tensor on EPIs. We further apply the computed disparity labels by fusing RGB Images and disparity labels to obtain more detailed over-segmentation. Meanwhile, the modified structure tensor algorithm is used to get more accurate Image boundaries, which plays a role in computing disparity features. All these processes are applied in an interactive segmentation model. Our experiments on public data sets demonstrate that the proposed light Field Image segmentation achieves a higher performance compared with state-of-the-art methods.