Objective C Download NEW! Image From Url
Ronna Srivastava
I have some images I need to get from the web. Just using data from a URL.
They need to show correctly on Retina Display.
When I get the images from the web, they still look pixelated. I need to set the images' scale to retina display (2.0), but I must be missing something. Here's what I did so far.
Your code should work pretty much as-is. I don't know what the original dimensions of your image were, but I'd guess they were 64x64 px. In order to scale down correctly, the original image would need to be 128x128 px.
(As an aside, I can't see that specifying @2x on the URL will help, in this case, as the call to dataWithContentsOfURL: will return an opaque blob of data, with no trace of the filename left. It's that opaque data that's then passed to imageWithData: to load the image.)
For retina display, add the same image with the resolution which is exactly the double of the original image. dont forget to add "@2x"at the end of this image name... e.g. "image_header.png" is an image 320x100 then another image with name "[email protected]" (dimension 640x200) will be selected for the retina display automatically by the OS...hope it helps
Methods: Eighty eyelids of 42 patients without meibomian gland loss (meiboscore=0), 105 eyelids of 57 patients with loss of less than one-third total meibomian gland area (meiboscore=1), 13 eyelids of 11 patients with between one-third and two-thirds loss of meibomian gland area (meiboscore=2) and 20 eyelids of 14 patients with two-thirds loss of meibomian gland area (meiboscore=3) were studied. Lid borders were automatically determined. The software evaluated the distribution of the luminance and, by enhancing the contrast and reducing image noise, the meibomian gland area was automatically discriminated. The software calculated the ratio of the total meibomian gland area relative to the total analysis area in all subjects. Repeatability of the software was also evaluated.
Conclusions: The meibomian gland area was objectively evaluated using the developed software. This system could be useful for objectively evaluating the effect of treatment on meibomian gland dysfunction.
Image quality metrics (IQMs) such as root mean square error (RMSE) and structural similarity index (SSIM) are commonly used in the evaluation and optimization of accelerated magnetic resonance imaging (MRI) acquisition and reconstruction strategies. However, it is unknown how well these indices relate to a radiologist's perception of diagnostic image quality. In this study, we compare the image quality scores of five radiologists with the RMSE, SSIM, and other potentially useful IQMs: peak signal to noise ratio (PSNR) multi-scale SSIM (MSSSIM), information-weighted SSIM (IWSSIM), gradient magnitude similarity deviation (GMSD), feature similarity index (FSIM), high dynamic range visible difference predictor (HDRVDP), noise quality metric (NQM), and visual information fidelity (VIF). The comparison uses a database of MR images of the brain and abdomen that have been retrospectively degraded by noise, blurring, undersampling, motion, and wavelet compression for a total of 414 degraded images. A total of 1017 subjective scores were assigned by five radiologists. IQM performance was measured via the Spearman rank order correlation coefficient (SROCC) and statistically significant differences in the residuals of the IQM scores and radiologists' scores were tested. When considering SROCC calculated from combining scores from all radiologists across all image types, RMSE and SSIM had lower SROCC than six of the other IQMs included in the study (VIF, FSIM, NQM, GMSD, IWSSIM, and HDRVDP). In no case did SSIM have a higher SROCC or significantly smaller residuals than RMSE. These results should be considered when choosing an IQM in future imaging studies.
As part of a clinical validation of a new brain-dedicated PET system (CMB), image quality of this scanner has been compared to that of a whole-body PET/CT scanner. To that goal, Hoffman phantom and patient data were obtined with both devices. Since CMB does not use a CT for attenuation correction (AC) which is crucial for PET images quality, this study includes the evaluation of CMB PET images using emission-based or CT-based attenuation maps. PET images were compared using 34 image quality metrics. Moreover, a neural network was used to evaluate the degree of agreement between both devices on the patients diagnosis prediction. Overall, results showed that CMB images have higher contrast and recovery coefficient but higher noise than PET/CT images. Although SUVr values presented statistically significant differences in many brain regions, relative differences were low. An asymmetry between left and right hemispheres, however, was identified. Even so, the variations between the two devices were minor. Finally, there is a greater similarity between PET/CT and CMB CT-based AC PET images than between PET/CT and the CMB emission-based AC PET images.
Here comes the question: did someone use Canon Eos Utility with just the camera body and a microscope, no objective attached? Is there a way to have the live image match the look of the image captured even when the camera body is used without an objective attached??
This work evaluated the performance of a detector-based spectral CT system by obtaining objective reference data, evaluating attenuation response of iodine and accuracy of iodine quantification, and comparing conventional CT and virtual monoenergetic images in three common phantoms. Scanning was performed using the hospital's clinical adult body protocol. Modulation transfer function (MTF) was calculated for a tungsten wire and visual line pair targets were evaluated. Image noise power spectrum (NPS) and pixel standard deviation were calculated. MTF for monoenergetic images agreed with conventional images within 0.05 lp cm-1. NPS curves indicated that noise texture of 70 keV monoenergetic images is similar to conventional images. Standard deviation measurements showed monoenergetic images have lower noise except at 40 keV. Mean CT number and CNR agreed with conventional images at 75 keV. Measured iodine concentration agreed with true concentration within 6% for inserts at the center of the phantom. Performance of monoenergetic images at detector based spectral CT is the same as, or better than, that of conventional images. Spectral acquisition and reconstruction with a detector based platform represents the physical behaviour of iodine as expected and accurately quantifies the material concentration.
In the past two decades there have been a lot of interests in both image and video processing. This is mainly due to the explosive growth of multimedia over the internet. Currently Cisco predicts by year 2022, more than 82% of the internet traffic will be video related material [3], let alone applications in social networks, which try to retrieve mages of various kinds in the net. Considering that raw image/video demand a large volume of data to be represented properly, their compression to achieve a manageable storage and transmission rate is inevitable. This is only possible at the cost of induced distortions in the processed image/video. It is highly desired to measure such distortions, by any objective measuring tool.
To resolve MOS limitations, historically image/video quality is measured based on the difference between their unprocessed and processed versions and presented in terms of Peak-Signal-to-Noise Ratio, PSNR. However, it can be argued that PSNR may not be a valid quality measure in certain scenarios. For instance, if the original non-distorted image is shifted even by one pixel, the difference between the original signal and its shifted version can show a significant drop in PSNR, whereas the shifted image quality is subjectively perfect. Moreover, PSNR value is not an indication of absolute acceptable video quality, nor it can be used to compare two different visual contents. Despite this, PSNR is a valid criterion in comparing image/video of the same content, provided their dimensions are not altered. In [5], it is shown that if the image content remains unaltered, improving PSNR can definitely improve MOS. This is the reason all video codecs, through rate-distortion optimization try to minimize coding distortion (maximize PSNR) for the best subjective quality.
Another group of IQAs known as perceptual meters are based on Structural Similarity Index (SSIM), which like PSNR they are based on FR. A variety of these perceptual meters have been developed but all have a common problem that they lose precision and accuracy at high image quality range. The main contribution of this paper is to show how Logistic Functions (LF) can improve the performance of these quality metrics. Through the experiments we show how LF can be easily added to all of these measuring tools, not only to improve their precision but also to increase their correlations to MOS.
For instance, in multi scale structural similarity [22] it is assumed that the human visual system adapts itself to extract structural information of the scene, and hence structural similarity can provide a good measure of perceived image quality. Weighting structural similarity for better adaptation is presented in IWSSIM [20]. In [27] Local weight is calculated based on the symmetry model of the reference image and more weights are given to certain areas. Using such criterion, [26] introduces VSI, a visual saliency-induced index for perceptual image quality assessment, where more weight is given in the pooling strategy. FSIM: a Feature Similarity Index for image quality assessment is described in [25]. Since human visual system is more sensitive to image edges, FSIM is mainly an edge-sensitive image quality assessor. The super-pixel method, known as SPSSIM, is another well-known and new model that divides images into meaningful areas and the evaluation model is based on the local quality of these areas [14]. Finally, an image quality assessment method based on edge-feature image segmentation (EFS) is proposed in [13].
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