32,343 research outputs found
Statistical evaluation of visual quality metrics for image denoising
This paper studies the problem of full reference visual quality assessment of
denoised images with a special emphasis on images with low contrast and
noise-like texture. Denoising of such images together with noise removal often
results in image details loss or smoothing. A new test image database, FLT,
containing 75 noise-free "reference" images and 300 filtered ("distorted")
images is developed. Each reference image, corrupted by an additive white
Gaussian noise, is denoised by the BM3D filter with four different values of
threshold parameter (four levels of noise suppression). After carrying out a
perceptual quality assessment of distorted images, the mean opinion scores
(MOS) are obtained and compared with the values of known full reference quality
metrics. As a result, the Spearman Rank Order Correlation Coefficient (SROCC)
between PSNR values and MOS has a value close to zero, and SROCC between values
of known full-reference image visual quality metrics and MOS does not exceed
0.82 (which is reached by a new visual quality metric proposed in this paper).
The FLT dataset is more complex than earlier datasets used for assessment of
visual quality for image denoising. Thus, it can be effectively used to design
new image visual quality metrics for image denoising.Comment: Submitted to ICASSP 201
Public Perception of Visual Quality of Cut Mutia Mosque Park as Public Space in Jakarta
Cut Mutia Park is a city park which is an integral part and attached to the main courtyard of the mosque cut mutia. This park is a green open space that serves as a public space and generate the aesthetics of the city. There are seven critical elements that need to be studied to determine the public\u27s perception of the park. Knowledgeable public perception of these elements, useful for city authorities in the development of city parks for future. The results of the research showed that of the seven elements studied, only two elements of which will be a positive perception, namely aspects of cleanliness and coolness, while five other aspects got a negative perception. As a result, generally Cut Mutia Parks research object only gets the value perception of -0.24. Details of the value obtained by each of these aspects are: -0.52 for comfortability; +0.13 for cleanliness; -0.003 for freshness; +0.26 for coolness; -0.77 for harmony level; -0.42 for beauty level; and -0.32 for interesting presented. Low perception from respondents indicated that Cut Mutia Park need better design such that it can give added value to the mosque and the environment surrounding. At the end can be said that the city authorities need to further improve attention in structuring of the park, to makes Cut Mutia Park become more comfortable, interesting and favored by the public, and also strengthening the power of the mosque as the central point
Visual-Quality-Driven Learning for Underwater Vision Enhancement
The image processing community has witnessed remarkable advances in enhancing
and restoring images. Nevertheless, restoring the visual quality of underwater
images remains a great challenge. End-to-end frameworks might fail to enhance
the visual quality of underwater images since in several scenarios it is not
feasible to provide the ground truth of the scene radiance. In this work, we
propose a CNN-based approach that does not require ground truth data since it
uses a set of image quality metrics to guide the restoration learning process.
The experiments showed that our method improved the visual quality of
underwater images preserving their edges and also performed well considering
the UCIQE metric.Comment: Accepted for publication and presented in 2018 IEEE International
Conference on Image Processing (ICIP
Exact Histogram Specification Optimized for Structural Similarity
An exact histogram specification (EHS) method modifies its input image to
have a specified histogram. Applications of EHS include image (contrast)
enhancement (e.g., by histogram equalization) and histogram watermarking.
Performing EHS on an image, however, reduces its visual quality. Starting from
the output of a generic EHS method, we maximize the structural similarity index
(SSIM) between the original image (before EHS) and the result of EHS
iteratively. Essential in this process is the computationally simple and
accurate formula we derive for SSIM gradient. As it is based on gradient
ascent, the proposed EHS always converges. Experimental results confirm that
while obtaining the histogram exactly as specified, the proposed method
invariably outperforms the existing methods in terms of visual quality of the
result. The computational complexity of the proposed method is shown to be of
the same order as that of the existing methods.
Index terms: histogram modification, histogram equalization, optimization for
perceptual visual quality, structural similarity gradient ascent, histogram
watermarking, contrast enhancement
Quality Adaptive Least Squares Trained Filters for Video Compression Artifacts Removal Using a No-reference Block Visibility Metric
Compression artifacts removal is a challenging problem because videos can be compressed at different qualities. In this paper, a least squares approach that is self-adaptive to the visual quality of the input sequence is proposed. For compression artifacts, the visual quality of an image is measured by a no-reference block visibility metric. According to the blockiness visibility of an input image, an appropriate set of filter coefficients that are trained beforehand is selected for optimally removing coding artifacts and reconstructing object details. The performance of the proposed algorithm is evaluated on a variety of sequences compressed at different qualities in comparison to several other deblocking techniques. The proposed method outperforms the others significantly both objectively and subjectively
An automatic technique for visual quality classification for MPEG-1 video
The Centre for Digital Video Processing at Dublin City University developed Fischlar [1], a web-based system for recording, analysis, browsing and playback of digitally captured television programs. One major issue for Fischlar is the automatic evaluation of video quality in order to avoid processing and storage of corrupted data. In this paper we propose an automatic classification technique that detects the video content quality in order to provide a decision criterion for the processing and storage stages
Visual Quality Enhancement in Optoacoustic Tomography using Active Contour Segmentation Priors
Segmentation of biomedical images is essential for studying and
characterizing anatomical structures, detection and evaluation of pathological
tissues. Segmentation has been further shown to enhance the reconstruction
performance in many tomographic imaging modalities by accounting for
heterogeneities of the excitation field and tissue properties in the imaged
region. This is particularly relevant in optoacoustic tomography, where
discontinuities in the optical and acoustic tissue properties, if not properly
accounted for, may result in deterioration of the imaging performance.
Efficient segmentation of optoacoustic images is often hampered by the
relatively low intrinsic contrast of large anatomical structures, which is
further impaired by the limited angular coverage of some commonly employed
tomographic imaging configurations. Herein, we analyze the performance of
active contour models for boundary segmentation in cross-sectional optoacoustic
tomography. The segmented mask is employed to construct a two compartment model
for the acoustic and optical parameters of the imaged tissues, which is
subsequently used to improve accuracy of the image reconstruction routines. The
performance of the suggested segmentation and modeling approach are showcased
in tissue-mimicking phantoms and small animal imaging experiments.Comment: Accepted for publication in IEEE Transactions on Medical Imagin
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