Tone mapping operators and multi-exposure fusion methods allow us to enjoy the informative contents of high dynamic range (HDR) images with standard dynamic range devices, but also introduce distortions into HDR contents. Therefore methods are needed to evaluate tone-mapped image quality. Due to the complexity of possible distortions in a tonemapped image, information from different scales and different levels should be considered when predicting tone-mapped image quality. So we propose a new no-reference method of tone-mapped image quality assessment based on multi-scale and multi-layer features that are extracted from a pre-trained deep convolutional neural network model. After being aggregated, the extracted features are mapped to quality predictions by regression. The proposed method is tested on the largest public database for TMIQA and compared to existing no-reference methods. The experimental results show that the proposed method achieves better performance.
Due to the importance of detail preservation, many researchers naturally take structural similarity between the original HDR images and the tone-mapped ones as a measure of tone-mapped image quality. @cite_14 first break the limitation of different dynamic range with proper transformation and measure the similarity between HDR and reproduced SDR images. @cite_1 propose the popular FR framework of Tone-Mapped image Quality Index (TMQI), which contains two components: multi-scale local structural similarity to evaluate the information preservation, and statistical naturalness to evaluate the natural looking of tone-mapped images. Many modified methods are proposed on the basis of TMQI. @cite_13 use visual saliency, while @cite_15 choose sparse domain instead, to modify the measurement of local structural similarity; @cite_7 use the statistical features of mean subtracted contrast normalized (MSCN) pixels to measure the natural statistics, and choose a pooling strategy from standard deviation of MSCN pixels, visual saliency and local information entropy for the pooling of local structural similarity.
@cite_14 @cite_7 @cite_1 @cite_15 @cite_13
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The diversity of display technologies and introduction of high dynamic range imagery introduces the necessity of comparing images of radically different dynamic ranges. Current quality assessment metrics are not suitable for this task, as they assume that both reference and test images have the same dynamic range. Image fidelity measures employed by a majority of current metrics, based on the difference of pixel intensity or contrast values between test and reference images, result in meaningless predictions if this assumption does not hold. We present a novel image quality metric capable of operating on an image pair where both images have arbitrary dynamic ranges. Our metric utilizes a model of the human visual system, and its central idea is a new definition of visible distortion based on the detection and classification of visible changes in the image structure. Our metric is carefully calibrated and its performance is validated through perceptual experiments. We demonstrate possible applications of our metric to the evaluation of direct and inverse tone mapping operators as well as the analysis of the image appearance on displays with various characteristics. Measuring visual quality, as perceived by human observers, is becoming increasingly important in the many applications in which humans are the ultimate consumers of visual information. This paper assesses the visual quality in mapping of high dynamic range (HDR) images to standard dynamic range (SDR) images with 8 bits color pixel. In previous work, the Tone-Mapped image Quality Index (TMQI) compares the original HDR image with the rendered SDR image. TMQI quantifies distortions locally and pools them by uniform averaging, in addition to measuring naturalness of the SDR image. For SDR images, perceptual pooling strategies have improved correlation of image quality assessment (IQA) algorithms with subjective scores. The primary contributions of this paper are: (1) integrating local information-based pooling strategies in the TMQI IQA algorithm, (2) measuring image naturalness by using mean-subtracted contrast-normalized pixels, and (3) testing the proposed methods on JPEG compressed tone-mapped images and tone-mapped images for SDR displays using subjective scores. Tone-mapping operators (TMOs) that convert high dynamic range (HDR) to low dynamic range (LDR) images provide practically useful tools for the visualization of HDR images on standard LDR displays. Different TMOs create different tone-mapped images, and a natural question is which one has the best quality. Without an appropriate quality measure, different TMOs cannot be compared, and further improvement is directionless. Subjective rating may be a reliable evaluation method, but it is expensive and time consuming, and more importantly, is difficult to be embedded into optimization frameworks. Here we propose an objective quality assessment algorithm for tone-mapped images by combining: 1) a multiscale signal fidelity measure on the basis of a modified structural similarity index and 2) a naturalness measure on the basis of intensity statistics of natural images. Validations using independent subject-rated image databases show good correlations between subjective ranking score and the proposed tone-mapped image quality index (TMQI). Furthermore, we demonstrate the extended applications of TMQI using two examples—parameter tuning for TMOs and adaptive fusion of multiple tone-mapped images.A Matlab code of the proposed algorithm is available online at https: ece.uwaterloo.ca z70wang research tmqi . Partial preliminary results of this work were presented at International Conference on Image Analysis and Recognition, Burnaby, BC, Canada, June 2011. Recently, an increasing number of tone-mapping operators (TMOs) have been proposed in order to display high dynamic nge (HDR) images on low dynamic range (LDR) devices. Developing perceptually consistent image quality assessment (QA) measures for TMO is highly desired because traditional LDR based IQA methods cannot support the cross dynamic range quality comparison. In this paper, a novel objective quality assessment method is proposed on the basis of sparse-domain representation, which has been well advocated as a powerful tool in describing natural sparse signals with the over-complete dictionary. Specifically, two indices, incorporating both local and global features extracted from sparsely represented coefficients, are introduced to simulate the human visual system (HVS) characteristics on HDR images. The local feature measures the sparse-domain similarity between the pristine HDR and tone-mapped L R images by leveraging the intrinsic structure with sparse coding. On the other hand, benefiting from the natural scene statistics (NSS), the global features are recovered from the sparse coefficients to account for the natural behaviors of tone-mapped images. Combining the local sparse-domain similarity and the global “naturalness” prior, validations on the public database show that the proposed sparse-domain model for tone-mapped images (SMTI) provides accurate predictions on the human perception of tone-mapped images. In this work, based on the local phase information of images, an objective index, called the feature similarity index for tone-mapped images (FSITM), is proposed. To evaluate a tone mapping operator (TMO), the proposed index compares the locally weighted mean phase angle map of an original high dynamic range (HDR) to that of its associated tone-mapped image calculated using the output of the TMO method. In experiments on two standard databases, it is shown that the proposed FSITM method outperforms the state-of-the-art index, the tone mapped quality index (TMQI). In addition, a higher performance is obtained by combining the FSITM and TMQI indices.