jameelah harbi - Academia.edu (original) (raw)
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RAJARAMBAPU INSTITUTE OF TECHNOLOGY,SAKHARALE MAHARASHTRA
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Without a reference image at hand, NR methods are more challenging. The vast majority of NR IQA a... more Without a reference image at hand, NR methods are more challenging. The vast majority of NR IQA algorithms aim to evaluate specific distortion, such as blocking, blur and ringing. Blocking artifacts are mainly caused by block-DCT based coding, such as JPEG and MPEG. The blocking artifacts are first modeled as a 2D step function. The artifacts visibility map is then estimated by oriented activities and the brightness of local background. Blocking artifacts are evaluated using the average difference across block boundaries, and blur is estimated by further combining intra-block activity.
Papers by jameelah harbi
Lecture Notes in Computer Science, 2006
No-reference perceptual quality assessment for JPEG images in real time is a critical requirement... more No-reference perceptual quality assessment for JPEG images in real time is a critical requirement for some applications, such as in-service visual quality monitoring, where original information can not be available. This paper proposes a no-reference perceptual quality-assessment method based on a general regression neural network (GRNN). The three visual features of artifacts introduced in JPEG images are formulated block by block individually so that our method is computational and memory-efficient. The GRNN is used to realize the mapping of these visual features into a quality score in real time because of its excellent approximation and very short training time (one-pass learning). Experimental results on an on-line database show that our estimated scores have an excellent correlation with subjective MOS scores.
Skin tone detection had been performed by using RGB color system, it is an interested filled of w... more Skin tone detection had been performed by using RGB color system, it is an interested filled of working due to the great hazard facing the digital world at this time. Many of the RGB images are the most common area of that detection. In this paper, an experimental study of face detection algorithms based on “Skin Color” has been introduce using Matlab environment to detect the skin color of an image. Having a true color image is the input, then the resulted image is a skin image that are detected by the algorithm that is built for that purpose.
Images captured under low light situations suffer from low contrast and low visibility which can ... more Images captured under low light situations suffer from low contrast and low visibility which can effect in bad manner on computer tasks to overcome this problem, enhancing low light image needed as pre-processing. This paper is presented a trainable model for low light image enhancing. It is based on multi scale Retinex by using deep learning and convolutional neural network (CNN) algorithm. Public (LOL) dataset has been used to train this model, consisted from 500 colored, low light images. Convolutional neural network bullied-up from eleven layers. SSIM and PSNR has been used to evaluate this model showing that average value of SSIM is (o.8) and average value of PSNR is and (21).
Without a reference image at hand, NR methods are more challenging. The vast majority of NR IQA a... more Without a reference image at hand, NR methods are more challenging. The vast majority of NR IQA algorithms aim to evaluate specific distortion, such as blocking, blur and ringing. Blocking artifacts are mainly caused by block-DCT based coding, such as JPEG and MPEG. The blocking artifacts are first modeled as a 2D step function. The artifacts visibility map is then estimated by oriented activities and the brightness of local background. Blocking artifacts are evaluated using the average difference across block boundaries, and blur is estimated by further combining intra-block activity.
Lecture Notes in Computer Science, 2006
No-reference perceptual quality assessment for JPEG images in real time is a critical requirement... more No-reference perceptual quality assessment for JPEG images in real time is a critical requirement for some applications, such as in-service visual quality monitoring, where original information can not be available. This paper proposes a no-reference perceptual quality-assessment method based on a general regression neural network (GRNN). The three visual features of artifacts introduced in JPEG images are formulated block by block individually so that our method is computational and memory-efficient. The GRNN is used to realize the mapping of these visual features into a quality score in real time because of its excellent approximation and very short training time (one-pass learning). Experimental results on an on-line database show that our estimated scores have an excellent correlation with subjective MOS scores.
Skin tone detection had been performed by using RGB color system, it is an interested filled of w... more Skin tone detection had been performed by using RGB color system, it is an interested filled of working due to the great hazard facing the digital world at this time. Many of the RGB images are the most common area of that detection. In this paper, an experimental study of face detection algorithms based on “Skin Color” has been introduce using Matlab environment to detect the skin color of an image. Having a true color image is the input, then the resulted image is a skin image that are detected by the algorithm that is built for that purpose.
Images captured under low light situations suffer from low contrast and low visibility which can ... more Images captured under low light situations suffer from low contrast and low visibility which can effect in bad manner on computer tasks to overcome this problem, enhancing low light image needed as pre-processing. This paper is presented a trainable model for low light image enhancing. It is based on multi scale Retinex by using deep learning and convolutional neural network (CNN) algorithm. Public (LOL) dataset has been used to train this model, consisted from 500 colored, low light images. Convolutional neural network bullied-up from eleven layers. SSIM and PSNR has been used to evaluate this model showing that average value of SSIM is (o.8) and average value of PSNR is and (21).