A review on high dynamic range (HDR) image quality assessment
, , , , und | 12. Juli 2021
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Online veröffentlicht: 12. Juli 2021
Seitenbereich: 1 - 17
Eingereicht: 10. Dez. 2020
DOI: https://doi.org/10.21307/ijssis-2021-010
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© 2021 Irwan Prasetya Gunawan et al., published by Sciendo
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
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Summary of MEF-based HDR images.
| Paper | Method | Strength | Weakness |
|---|---|---|---|
| Multi-exposure fusion algorithm | Well correlates with subjective judgments and significantly outperforms the existing IQA models for general image fusion | Cannot apply on a various image content | |
| Gradient-based synthesized multiple exposure | Produces good quality HDR images from a series of poor quality photos taken by various exposures | Cannot apply on a colored image | |
| New multiple exposure time image synthesization technique | High-quality color HDR image which contains the maximum level of details and RGB color information | The current implementation of the proposed method is limited to process static scenes | |
| Fused gradient field | This method is efficient and effective | Existing algorithms can only be used for small movements | |
| New quadratic optimization | Can enhance fine detail to produce sharper images as existing high dynamic range imaging schemes | Saturation images sometimes reduced by using both proposed exposure fusion schemes | |
| New probabilistic exposure fusion scheme | New approach is advantageous compared with representative existing tone mapping operators | Rating and ranking are not suitable because both are too complex for an observer | |
| A novel fusion algorithm based on perceptual quality measures | Experiments demonstrated better performance of proposed algorithm compared to other methods | It is relatively difficult to extend these metrics to cases with several image sources | |
| Fuse multi-exposure images of a static scene taken by a stationary camera | It has no side effect and the local color and contrast in the input will not change | Select images to be mixed, the right size must be used to fuse the image | |
| Fuse a bracket exposure sequence | Comparable to the existing tone mapping operator | Unoptimized implementation of software performs fusion of exposure within seconds | |
| Single exposure-based image fusion using multi-transformation | Shows a more visually pleasing output with the perceptually increased dynamic range | – | |
| A new color multi-exposure image fusion | Successfully producing a better color display from the image blends and more texture details than other existing exposure fusion techniques | Based on the proposed approach, MEF cannot yet combine dynamic multi-exposure images and eliminate them | |
| A new multi-exposure image fusion method based on exposure compensation | Better than other methods in terms of TMQI, statistical naturalness and discrete entropy | It is unclear how to determine appropriate exposure values, which are difficult to set at the time of photography |
Summary of contour detection methods.
| Paper | Method | Advantage | Drawback |
|---|---|---|---|
| False contour candidate in HEVC | Detecting very noticeable, remove and preseving texture and details | false remove false contour in larger sized | |
| Flat-region and bit-depth extension | Removes false contour effectively and preserving sharpness | Cannot remove the local holelike pattern effectively | |
| Gaussian filter and spectral clustering | Enhancing peak level and smoothing direction | Contour detection only generates only around a strong boundary | |
| MHEC (Harris for edge and corners) point set | Handle complex contour, ability for multiple object detection | Iterative active contour still slower than other method | |
| CNN patch-level measurement | No need precisely predict boundary pixel | At large texture regions still erroneous |
Summary of several HDR IQA methods.
| Authors | Methods | Databases | Metrics |
|---|---|---|---|
| Full-reference error metrics | LIVE, TID2008 | HDR-VDP-2 | |
| Full-reference, tone-mapped images, multi-scale SSIM | Own dataset ( | TMQI | |
| Full-reference, MEF images | Own dataset ( | MEF-IQA | |
| No-reference, natural scene statistics | ESPL-LIVE | HIGRADE | |
| No-reference, DL, convolutional neural networks with saliency maps | LIVE and CSIQ (SDR) | DL-NRIQA | |
| No-reference, tensor space, image manifold | Publicly available dataset | TDML with SVR-based | |
| Convolutional neural nets, SVM, tone mapping, deep no-reference tone-mapped image quality assessment, NRIQA | ESPL-LIVE and Yeganeh | RcNet | |
| Feature extraction; support vector machines; tone-mapped HDR; multi-exposure fused images; no-reference (NR); colorfulness, exposure, naturalness | Publicly available dataset | SVM-based features | |
| Local dimming algorithms, image contrast ratio, subjective, objective | Fairchild’s | BLD algorithms | |
| MEF algorithms; objective quality model; reduced ghosting artifacts; Heuristic algorithms; structural similarity | Own dataset and Mantiuk’s MEF deghosting images | MEF-SSIM_d | |
| Convolutional neural nets; learning-based RTM scheme; low-complexity reverse tone mapping | Own dataset | RTM Scheme, HDR-VQM | |
| Entropy; feature extraction; support vector machines; colorfulness index; tone mapping operators; luminance partition; NRIQA | TMID and ESPL-LIVE | SVR-based | |
| HMM, TMO, FR | ETHyma | HMM-based similarity measure | |
| TMO, FR, NR, feature naturalness, structural similarity, and feature similarity | Yeganeh, Cadik, and TMIQD | FFTMI, based on SS-II, FN, and FSITM | |
| NRIQA, tone-mapped images | TMID and ESPL-LIVE | SVR-based with RBF kernel | |
| NRIQA, tone-mapped images, gradient, chromatics, statistics | ESPL-LIVE | VQGC |
Summary of ITMO-based HDR images.
| Paper | Method | Strength | Weakness |
|---|---|---|---|
| Tone reproduction curves (TRC) | Performs well on a wide category of images | Produced a visual accurate images but not enhanced images | |
| Zone system and Automatic dodging and burning | Well-suited on a across-the-board of HDR images | This method only brought textured areas within range which is categorized simple | |
| Extended version of | Solve interesting problem in TMO | This system is slower than its state-of-the-art method | |
| A gradient-based tone mapping operator | Able to compress a very wide dynamic range, present every details and less common noise or artifacts | Does not enhance global features | |
| Contrast mapping and contrast equalization | Provide a high visual quality output with appealing brighness and contrast even no artifacts | Does not run in real-time application and does not include color in information | |
| Optimized tone reproduction curve (TRC) | More simple than the previous, faster in time consuming and easier to implement | Weak at destroying spatial details | |
| A real-time noise TMO | Minimize the contrast disortions, control the perceptibility of noises and adjust to a provide and shifting light, also can be apply in real-time | Lack in scenery creation and best subjective score | |
| SVR | Gained a consistent result under complex real-world ilumination transitions | The execution time are the longest among the-state-of-the-art | |
| Local tone mapping | Present details and good local and global contrast of proceed images also better result in overall image quality | Produce a little amount of noise |
Summary of several subjective assessment methods.
| Paper | Method | Description |
|---|---|---|
| Category scaling | Numerical category scaling techniques provide an efficient and valid way to get a compression ratio versus a quality curve and to assess the image quality perceived in a much smaller way | |
| Th. Alpert (CCETT) and J.-P. Evain (EBU) ( | SSCQE and DSCQE | SSCQE to evaluate subjective quality, while the DCSQE is used to maintain image quality and information transmitted |
| Double stimulus | The experiment used a double-stimulus methodology to measure quality more accurately for realignment purposes | |
| SS and QR | Single stimulus (SS) method presents several weaknesses. Quality ruler (QR) method is worth implementing efforts from the point of view of consistency and repetition of scores | |
| Force-choice pairwise comparison | The forced-choice pairwise comparison method results in the smallest measurement variance and thus produces the most accurate results. This method is also the most time-efficient, assuming a moderate number of compared conditions | |
| QR | The difference in assessment in the study seemed to be significantly dependent on the perceived similarity between the ruler image and the test image | |
| Dynamic reference | The DR method is very suitable for experiments that require very accurate results in a short time because the DR method is more accurate than the ACR method and faster than the PC method | |
| AIT inspired MOS and PC | Using arrow’s impossibility theorem (AIT) proves that the meeting between unanimity and independence of irrelevant alternatives (IIA) will produce an ‘important subject’, which in fact determines the final rating of image quality |