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Sjöström, Mårten
Publications (10 of 116) Show all publications
Li, Y., Olsson, R. & Sjöström, M. (2018). An analysis of demosaicing for plenoptic capture based on ray optics. In: Proceedings of 3DTV Conference 2018: . Paper presented at 3D at any scale and any perspective, 3-5 June 2018, Stockholm – Helsinki – Stockholm.
Open this publication in new window or tab >>An analysis of demosaicing for plenoptic capture based on ray optics
2018 (English)In: Proceedings of 3DTV Conference 2018, 2018Conference paper, Published paper (Refereed)
Abstract [en]

The plenoptic camera is gaining more and more attention as it capturesthe 4D light field of a scene with a single shot and enablesa wide range of post-processing applications. However, the preprocessing steps for captured raw data, such as demosaicing, have been overlooked. Most existing decoding pipelines for plenoptic cameras still apply demosaicing schemes which are developed for conventional cameras. In this paper, we analyze the sampling pattern of microlens-based plenoptic cameras by ray-tracing techniques and ray phase space analysis. The goal of this work is to demonstrate guidelines and principles for demosaicing the plenoptic captures by taking the unique microlens array design into account. We show that the sampling of the plenoptic camera behaves differently from that of a conventional camera and the desired demosaicing scheme is depth-dependent.

Keywords
Light field, plenoptic camera, depth, image demosaicing
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:miun:diva-33618 (URN)978-1-5386-6125-3 (ISBN)
Conference
3D at any scale and any perspective, 3-5 June 2018, Stockholm – Helsinki – Stockholm
Available from: 2018-05-15 Created: 2018-05-15 Last updated: 2018-06-12Bibliographically approved
Li, Y., Scrofani, G., Sjöström, M. & Martinez-Corraly, M. (2018). Area-Based Depth Estimation for Monochromatic Feature-Sparse Orthographic Capture. In: : . Paper presented at EUSIPCO 2018, 26th European Signal Processing Conference, Rome, Italy, September 3-7, 2018. IEEE conference proceedings
Open this publication in new window or tab >>Area-Based Depth Estimation for Monochromatic Feature-Sparse Orthographic Capture
2018 (English)Conference paper, Published paper (Refereed)
Abstract [en]

With the rapid development of light field technology, depth estimation has been highlighted as one of the critical problems in the field, and a number of approaches have been proposed to extract the depth of the scene. However, depthestimation by stereo matching becomes difficult and unreliable when the captured images lack both color and feature information. In this paper, we propose a scheme that extracts robust depth from monochromatic, feature-sparse scenes recorded in orthographic sub-aperture images. Unlike approaches which relyon the rich color and texture information across the sub-aperture views, our approach is based on depth from focus techniques. First, we superimpose shifted sub-aperture images on top of anarbitrarily chosen central image. To focus on different depths, the shift amount is varied based on the micro-lens array properties. Next, an area-based depth estimation approach is applied tofind the best match among the focal stack and generate the dense depth map. This process is repeated for each sub-aperture image. Finally, occlusions are handled by merging depth maps generated from different central images followed by a voting process. Results show that the proposed scheme is more suitable than conventional depth estimation approaches in the context of orthographic captures that have insufficient color and feature information, such as microscopic fluorescence imaging.

Place, publisher, year, edition, pages
IEEE conference proceedings, 2018
Keywords
Depth estimation, integral imaging, orthographic views, depth from focus
National Category
Computer Sciences
Identifiers
urn:nbn:se:miun:diva-34418 (URN)
Conference
EUSIPCO 2018, 26th European Signal Processing Conference, Rome, Italy, September 3-7, 2018
Available from: 2018-09-14 Created: 2018-09-14 Last updated: 2018-09-19Bibliographically approved
Ahmad, W., Sjöström, M. & Olsson, R. (2018). Compression scheme for sparsely sampled light field data based on pseudo multi-view sequences. In: SPIE Photonics Europe 2018: Proceeding. Paper presented at SPIE Photonics Europe 2018 Strasbourg, France, 22-26 April 2018.
Open this publication in new window or tab >>Compression scheme for sparsely sampled light field data based on pseudo multi-view sequences
2018 (English)In: SPIE Photonics Europe 2018: Proceeding, 2018Conference paper, Published paper (Refereed)
Abstract [en]

With the advent of light field acquisition technologies, the captured information of the scene is enriched by having both angular and spatial information. The captured information provides additional capabilities in the post processing stage, e.g. refocusing, 3D scene reconstruction, synthetic aperture etc. Light field capturing devices are classified in two categories. In the first category, a single plenoptic camera is used to capture a densely sampled light field, and in second category, multiple traditional cameras are used to capture a sparsely sampled light field. In both cases, the size of captured data increases with the additional angular information. The recent call for proposal related to compression of light field data by JPEG, also called “JPEG Pleno”, reflects the need of a new and efficient light field compression solution. In this paper, we propose a compression solution for sparsely sampled light field data. In a multi-camera system, each view depicts the scene from a single perspective. We propose to interpret each single view as a frame of pseudo video sequence. In this way, complete MxN views of multi-camera system are treated as M pseudo video sequences, where each pseudo video sequence contains N frames. The central pseudo video sequence is taken as base View and first frame in all the pseudo video sequences is taken as base Picture Order Count (POC). The frame contained in base view and base POC is labeled as base frame. The remaining frames are divided into three predictor levels. Frames placed in each successive level can take prediction from previously encoded frames. However, the frames assigned with last prediction level are not used for prediction of other frames. Moreover, the rate-allocation for each frame is performed by taking into account its predictor level, its frame distance and view wise decoding distance relative to the base frame. The multi-view extension of high efficiency video coding (MV-HEVC) is used to compress the pseudo multi-view sequences. The MV-HEVC compression standard enables the frames to take prediction in both direction (horizontal and vertical d), and MV-HEVC parameters are used to implement the proposed 2D prediction and rate allocation scheme. A subset of four light field images from Stanford dataset are compressed, using the proposed compression scheme on four bitrates in order to cover the low to high bit-rates scenarios. The comparison is made with state-of-art reference encoder HEVC and its real-time implementation X265. The 17x17 grid is converted into a single pseudo sequence of 289 frames by following the order explained in JPEG Pleno call for proposal and given as input to the both reference schemes. The rate distortion analysis shows that the proposed compression scheme outperforms both reference schemes in all tested bitrate scenarios for all test images. The average BD-PSNR gain is 1.36 dB over HEVC and 2.15 dB over X265.

Keywords
Light field, MV-HEVC, Compression, Plenoptic, Multi-Camera
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:miun:diva-33352 (URN)
Conference
SPIE Photonics Europe 2018 Strasbourg, France, 22-26 April 2018
Available from: 2018-03-26 Created: 2018-03-26 Last updated: 2018-06-11Bibliographically approved
Domanski, M., Grajek, T., Conti, C., Debono, C. J., de Faria, S. M. M., Kovacs, P., . . . Stankiewicz, O. (2018). Emerging Imaging Technologies: Trends and Challenges. In: Assunção, Pedro Amado, Gotchev, Atanas (Ed.), 3D Visual Content Creation, Coding and Delivery: (pp. 5-39). Cham: Springer
Open this publication in new window or tab >>Emerging Imaging Technologies: Trends and Challenges
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2018 (English)In: 3D Visual Content Creation, Coding and Delivery / [ed] Assunção, Pedro Amado, Gotchev, Atanas, Cham: Springer, 2018, p. 5-39Chapter in book (Refereed)
Place, publisher, year, edition, pages
Cham: Springer, 2018
Series
Signals and Communication Technology, ISSN 1860-4862
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:miun:diva-34379 (URN)978-3-319-77842-6 (ISBN)
Available from: 2018-09-13 Created: 2018-09-13 Last updated: 2018-09-13
Dima, E., Sjöström, M., Olsson, R., Kjellqvist, M., Litwic, L., Zhang, Z., . . . Flodén, L. (2018). LIFE: A Flexible Testbed For Light Field Evaluation. In: : . Paper presented at 2018 3DTV Conference: The True Vision - Capture, Transmission and Display of 3D Video (3DTV-CON), Stockholm – Helsinki – Stockholm, 3-5 June 2018.
Open this publication in new window or tab >>LIFE: A Flexible Testbed For Light Field Evaluation
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2018 (English)Conference paper, Published paper (Refereed)
Abstract [en]

Recording and imaging the 3D world has led to the use of light fields. Capturing, distributing and presenting light field data is challenging, and requires an evaluation platform. We define a framework for real-time processing, and present the design and implementation of a light field evaluation system. In order to serve as a testbed, the system is designed to be flexible, scalable, and able to model various end-to-end light field systems. This flexibility is achieved by encapsulating processes and devices in discrete framework systems. The modular capture system supports multiple camera types, general-purpose data processing, and streaming to network interfaces. The cloud system allows for parallel transcoding and distribution of streams. The presentation system encapsulates rendering and display specifics. The real-time ability was tested in a latency measurement; the capture and presentation systems process and stream frames within a 40 ms limit.

Keywords
Multiview, 3DTV, Light field, Distributed surveillance, 360 video
National Category
Computer and Information Sciences
Identifiers
urn:nbn:se:miun:diva-33620 (URN)
Conference
2018 3DTV Conference: The True Vision - Capture, Transmission and Display of 3D Video (3DTV-CON), Stockholm – Helsinki – Stockholm, 3-5 June 2018
Projects
LIFE Project
Funder
Knowledge Foundation, 20140200
Available from: 2018-05-15 Created: 2018-05-15 Last updated: 2018-05-16Bibliographically approved
Conti, C., Soares, L. D., Nunes, P., Perra, C., Assunção, P. A., Sjöström, M., . . . Jennehag, U. (2018). Light Field Image Compression. In: Assunção, Pedro Amado, Gotchev, Atanas (Ed.), 3D Visual Content Creation, Coding and Delivery: (pp. 143-176). Cham: Springer
Open this publication in new window or tab >>Light Field Image Compression
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2018 (English)In: 3D Visual Content Creation, Coding and Delivery / [ed] Assunção, Pedro Amado, Gotchev, Atanas, Cham: Springer, 2018, p. 143-176Chapter in book (Refereed)
Place, publisher, year, edition, pages
Cham: Springer, 2018
Series
Signals and Communication Technology, ISSN 1860-4862
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:miun:diva-34382 (URN)978-3-319-77842-6 (ISBN)
Available from: 2018-09-13 Created: 2018-09-13 Last updated: 2018-09-13
Ahmad, W., Palmieri, L., Koch, R. & Sjöström, M. (2018). Matching Light Field Datasets From Plenoptic Cameras 1.0 And 2.0. In: Proceedings of the 2018 3DTV Conference: . Paper presented at 2018 3DTV Conference: The True Vision - Capture, Transmission and Display of 3D Video (3DTV-CON), Stockholm – Helsinki – Stockholm, 3-5 June 2018.
Open this publication in new window or tab >>Matching Light Field Datasets From Plenoptic Cameras 1.0 And 2.0
2018 (English)In: Proceedings of the 2018 3DTV Conference, 2018Conference paper, Published paper (Refereed)
Abstract [en]

The capturing of angular and spatial information of the scene using single camera is made possible by new emerging technology referred to as plenoptic camera. Both angular and spatial information, enable various post-processing applications, e.g. refocusing, synthetic aperture, super-resolution, and 3D scene reconstruction. In the past, multiple traditional cameras were used to capture the angular and spatial information of the scene. However, recently with the advancement in optical technology, plenoptic cameras have been introduced to capture the scene information. In a plenoptic camera, a lenslet array is placed between the main lens and the image sensor that allows multiplexing of the spatial and angular information onto a single image, also referred to as plenoptic image. The placement of the lenslet array relative to the main lens and the image sensor, results in two different optical design sof a plenoptic camera, also referred to as plenoptic 1.0 and plenoptic 2.0. In this work, we present a novel dataset captured with plenoptic 1.0 (Lytro Illum) and plenoptic 2.0(Raytrix R29) cameras for the same scenes under the same conditions. The dataset provides the benchmark contents for various research and development activities for plenoptic images.

Keywords
Plenoptic, Light-field, Dataset
Identifiers
urn:nbn:se:miun:diva-33764 (URN)978-1-5386-6125-3 (ISBN)
Conference
2018 3DTV Conference: The True Vision - Capture, Transmission and Display of 3D Video (3DTV-CON), Stockholm – Helsinki – Stockholm, 3-5 June 2018
Available from: 2018-06-13 Created: 2018-06-13 Last updated: 2018-06-14Bibliographically approved
Brunnström, K., Sjöström, M., Imran, M., Pettersson, M. & Johanson, M. (2018). Quality Of Experience For A Virtual Reality Simulator. In: Human Vision and Electronic Imaging (HVEI): . Paper presented at Human Vision and Electronic Imaging (HVEI), Burlingame, California USA, 28 January - 2 February, 2018.
Open this publication in new window or tab >>Quality Of Experience For A Virtual Reality Simulator
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2018 (English)In: Human Vision and Electronic Imaging (HVEI), 2018Conference paper, Published paper (Refereed)
Abstract [en]

In this study, we investigate a VR simulator of a forestrycrane used for loading logs onto a truck, mainly looking at Qualityof Experience (QoE) aspects that may be relevant for taskcompletion, but also whether there are any discomfort relatedsymptoms experienced during task execution. The QoE test hasbeen designed to capture both the general subjective experience ofusing the simulator and to study task completion rate. Moreover, aspecific focus has been to study the effects of latency on thesubjective experience, with regards both to delays in the cranecontrol interface as well as lag in the visual scene rendering in thehead mounted display (HMD). Two larger formal subjectivestudies have been performed: one with the VR-system as it is andone where we have added controlled delay to the display updateand to the joystick signals. The baseline study shows that mostpeople are more or less happy with the VR-system and that it doesnot have strong effects on any symptoms as listed in the SSQ. In thedelay study we found significant effects on Comfort Quality andImmersion Quality for higher Display delay (30 ms), but verysmall impact of joystick delay. Furthermore, the Display delay hadstrong influence on the symptoms in the SSQ, as well as causingtest subjects to decide not to continue with the completeexperiments, and this was also found to be connected to the longerDisplay delays (≥ 20 ms).

Keywords
Quality of Experience, Virtual Reality, simulator, Remote operation
National Category
Media and Communication Technology
Identifiers
urn:nbn:se:miun:diva-33073 (URN)
Conference
Human Vision and Electronic Imaging (HVEI), Burlingame, California USA, 28 January - 2 February, 2018
Funder
Knowledge Foundation, 20160194
Available from: 2018-02-26 Created: 2018-02-26 Last updated: 2018-09-19Bibliographically approved
Ahmad, W., Vagharshakyan, S., Sjöström, M., Gotchev, A., Bregovic, R. & Olsson, R. (2018). Shearlet Transform Based Prediction Scheme for Light Field Compression. In: : . Paper presented at Data Compression Conference (DCC 2018),Snowbird, Utah, US, March 27 - March 30, 2018.
Open this publication in new window or tab >>Shearlet Transform Based Prediction Scheme for Light Field Compression
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2018 (English)Conference paper, Poster (with or without abstract) (Refereed)
Abstract [en]

Light field acquisition technologies capture angular and spatial information ofthe scene. The spatial and angular information enables various post processingapplications, e.g. 3D scene reconstruction, refocusing, synthetic aperture etc at theexpense of an increased data size. In this paper, we present a novel prediction tool forcompression of light field data acquired with multiple camera system. The captured lightfield (LF) can be described using two plane parametrization as, L(u, v, s, t), where (u, v)represents each view image plane coordinates and (s, t) represents the coordinates of thecapturing plane. In the proposed scheme, the captured LF is uniformly decimated by afactor d in both directions (in s and t coordinates), resulting in a sparse set of views alsoreferred to as key views. The key views are converted into a pseudo video sequence andcompressed using high efficiency video coding (HEVC). The shearlet transform basedreconstruction approach, presented in [1], is used at the decoder side to predict thedecimated views with the help of the key views.Four LF images (Truck, Bunny from Stanford dataset, Set2 and Set9 from High DensityCamera Array dataset) are used in the experiments. Input LF views are converted into apseudo video sequence and compressed with HEVC to serve as anchor. Rate distortionanalysis shows the average PSNR gain of 0.98 dB over the anchor scheme. Moreover, inlow bit-rates, the compression efficiency of the proposed scheme is higher compared tothe anchor and on the other hand the performance of the anchor is better in high bit-rates.Different compression response of the proposed and anchor scheme is a consequence oftheir utilization of input information. In the high bit-rate scenario, high quality residualinformation enables the anchor to achieve efficient compression. On the contrary, theshearlet transform relies on key views to predict the decimated views withoutincorporating residual information. Hence, it has inherit reconstruction error. In the lowbit-rate scenario, the bit budget of the proposed compression scheme allows the encoderto achieve high quality for the key views. The HEVC anchor scheme distributes the samebit budget among all the input LF views that results in degradation of the overall visualquality. The sensitivity of human vision system toward compression artifacts in low-bitratecases favours the proposed compression scheme over the anchor scheme.

National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:miun:diva-33356 (URN)
Conference
Data Compression Conference (DCC 2018),Snowbird, Utah, US, March 27 - March 30, 2018
Available from: 2018-03-27 Created: 2018-03-27 Last updated: 2018-06-11
Ahmad, W., Palmieri, L., Koch, R. & Sjöström, M. (2018). The Plenoptic Dataset.
Open this publication in new window or tab >>The Plenoptic Dataset
2018 (English)Data set, Primary data
Abstract [en]

The dataset is captured using two different plenoptic cameras, namely Illum from Lytro (based on plenoptic 1.0 model) and R29 from Raytrix (based on plenoptic 2.0 model). The scenes selected for the dataset were captured under controlled conditions. The cameras were mounted onto a multi-camera rig that was mechanically controlled to move the cameras with millimeter precision. In this way, both cameras captured the scene from the same viewpoint.

Keywords
Plenoptic images, light field, Plenoptic Dataset, Light field dataset, Illum, Raytrix, plenoptic 1.0, plenoptic 2.0
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:miun:diva-33464 (URN)10.6084/m9.figshare.6115487 (DOI)
Funder
EU, Horizon 2020, No 676401
Available from: 2018-04-10 Created: 2018-04-10 Last updated: 2018-06-11
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