High Dynamic Range Imaging and Low Dynamic Range Expansion for Generating HDR Content

Banterle, Francesco; Debattista, Kurt; Artusi, Alessandro; Pattanaik, Sumanta; Myszkowski, Karol; Ledda, Patrick; Chalmers, Alan

doi:10.1111/j.1467-8659.2009.01541.x

Cited by 69 publications

(40 citation statements)

References 90 publications

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“…An alternative approach for speeding up Reinhard et al's operator would be to approximate the local part using a real-time bilateral filter [Banterle et al 2012b]. Another interesting direction would be to apply the proposed approach to inverse tone mapping (ITM) [Banterle et al 2009]. Many ITM operators have been proposed, they all have strength and weak points depending on which kind of image is applied to, i.e.…”

Section: Discussionmentioning

confidence: 99%

Dynamic range compression by differential zone mapping based on psychophysical experiments

Banterle

Artusi

Šikudová

et al. 2012

Proceedings of the ACM Symposium on Applied Perception

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In this paper we present a new technique for the display of High Dynamic Range (HDR) images on Low Dynamic Range (LDR) displays. The described process has three stages. First, the input image is segmented into luminance zones. Second, the tone mapping operator (TMO) that performs better in each zone is automatically selected. Finally, the resulting tone mapping (TM) outputs for each zone are merged, generating the final LDR output image. To establish the TMO that performs better in each luminance zone we conducted a preliminary psychophysical experiment using a set of HDR images and six different TMOs. We validated our composite technique on several (new) HDR images and conducted a further psychophysical experiment, using an HDR display as reference, that establishes the advantages of our hybrid three-stage approach over a traditional individual TMO.

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Section: Discussionmentioning

confidence: 99%

Dynamic range compression by differential zone mapping based on psychophysical experiments

Banterle

Artusi

Šikudová

et al. 2012

Proceedings of the ACM Symposium on Applied Perception

Self Cite

View full text Add to dashboard Cite

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“…To reduce noise, the residual stream is cross-bilaterally filtered [13]. Other proposed two-stream methods include goHDR [6] and optimal exposure [12].…”

Section: Related Workmentioning

confidence: 99%

“…Based on the peaks of the graph, and similarities to the GDF and PQ (see Sect. 3.3), the four implementations of PTF chosen for testing were: PTF 2.2 , PTF 4 , PTF 6 and PTF 8 .…”

Section: Hdr-vdp-221 (Hdr Visual Difference Predictor) Ismentioning

confidence: 99%

An evaluation of power transfer functions for HDR video compression

et al. 2016

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High dynamic range (HDR) imaging enables the full range of light in a scene to be captured, transmitted and displayed. However, uncompressed 32-bit HDR is four times larger than traditional low dynamic range (LDR) imagery. If HDR is to fulfil its potential for use in live broadcasts and interactive remote gaming, fast, efficient compression is necessary for HDR video to be manageable on existing communications infrastructure. A number of methods have been put forward for HDR video compression. However, these can be relatively complex and frequently require the use of multiple video streams. In this paper, we propose the use of a straightforward Power Transfer Function (PTF) as a practical, computationally fast, HDR video compression solution. The use of PTF is presented and evaluated against four other HDR video compression methods. An objective evaluation shows that PTF exhibits improved quality at a range of bitrates and, due to its straightforward nature, is highly suited for real-time HDR video applications.

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“…This method is highly restrictive so it does not lead to accurate results and does not support most real CRFs. 22 …”

Section: Inverse Camera Response Functionmentioning

confidence: 99%

“…5,22 They provide an excellent introduction to HDR imaging and analyze many important issues in this field.…”

mentioning

confidence: 99%

Full high-dynamic range images for dynamic scenes

et al. 2012

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The limited dynamic range of digital images can be extended by composing photographs of the same scene taken with the same camera at the same view point at different exposure times. This is a standard procedure for static scenes but a challenging task for dynamic ones. Several methods have been presented but few recover high dynamic range within moving areas. We present a method to recover full high dynamic range (HDR) images from dynamic scenes, even in moving regions. Our method has 3 steps. Firstly, areas affected by motion are detected to generate a ghost mask. Secondly, we register dynamic objects over a reference image (the best exposed image in the input sequence). Thirdly, we combine the registered input photographs to recover HDR values in a whole image using a weighted average function. Once matching is found, the assembling step guarantees that all aligned pixels will contribute to the final result, including dynamic content. Tests were made on more than 20 sets of sequences, with moving cars or pedestrians and different background. Our results show that Image Mapping Function approach detects best motion regions while Normalized Cross Correlation offers the best deal speed-accuracy for image registration. Results from our method offers better result when moving object are roughly rigid and their movement is mostly rigid. The final composition is an HDR image with no ghosting and all dynamic content present in HDR values.

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High Dynamic Range Imaging and Low Dynamic Range Expansion for Generating HDR Content

Abstract: In the last few years, researchers in the field of High Dynamic Range (HDR) Imaging have focused on providing tools for expanding Low Dynamic Range (LDR) content for the generation of HDR images

Cited by 69 publications

References 90 publications

Dynamic range compression by differential zone mapping based on psychophysical experiments

Dynamic range compression by differential zone mapping based on psychophysical experiments

An evaluation of power transfer functions for HDR video compression

Full high-dynamic range images for dynamic scenes

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