Applicability of Existing Objective Metrics of Perceptual Quality for Adaptive Video Streaming

Sogaard, Jacob; Krasula, Lukáš; Shahid, Muhammad; Temel, Dogancan; Brunnström, Kjell; Razaak, Manzoor

doi:10.2352/issn.2470-1173.2016.13.iqsp-206

Cited by 33 publications

(14 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The error is the difference between the estimator and estimated outcome. It is a function of risk, considering the expected value of the squared error loss or quadratic loss.Mean Squared Error (MSE) between two images such as is defined as[6]…”

mentioning

confidence: 99%

Image Quality Assessment through FSIM, SSIM, MSE and PSNR—A Comparative Study

Sara¹,

Akter²,

Uddin³

2019

JCC

1,005

347

View full text Add to dashboard Cite

Quality is a very important parameter for all objects and their functionalities. In image-based object recognition, image quality is a prime criterion. For authentic image quality evaluation, ground truth is required. But in practice, it is very difficult to find the ground truth. Usually, image quality is being assessed by full reference metrics, like MSE (Mean Square Error) and PSNR (Peak Signal to Noise Ratio). In contrast to MSE and PSNR, recently, two more full reference metrics SSIM (Structured Similarity Indexing Method) and FSIM (Feature Similarity Indexing Method) are developed with a view to compare the structural and feature similarity measures between restored and original objects on the basis of perception. This paper is mainly stressed on comparing different image quality metrics to give a comprehensive view. Experimentation with these metrics using benchmark images is performed through denoising for different noise concentrations. All metrics have given consistent results. However, from representation perspective, SSIM and FSIM are normalized, but MSE and PSNR are not; and from semantic perspective, MSE and PSNR are giving only absolute error; on the other hand, SSIM and PSNR are giving perception and saliency-based error. So, SSIM and FSIM can be treated more understandable than the MSE and PSNR.

show abstract

mentioning

confidence: 99%

Image Quality Assessment through FSIM, SSIM, MSE and PSNR—A Comparative Study

Sara¹,

Akter²,

Uddin³

2019

JCC

1,005

347

View full text Add to dashboard Cite

show abstract

“…In addition, a problem often encountered in road segmentation is the blurriness of the results. The structural similarity index metric is a powerful tool for image quality assessment [26]. Generally, a higher SSIM means cleaner results [25].…”

Section: Ssim Lossmentioning

confidence: 99%

“…The SSIM is a method for predicting the perceived quality of digital images, and it was originally introduced for assessing a structural similarity in the spatial domain. It is widely used because the human visual system is more sensitive to structures than pixels [26]. By reducing the SSIM loss and by achieving a higher SSIM score, the result of segmentation is an image with a better quality.…”

Section: Introductionmentioning

confidence: 99%

Road Extraction by Using Atrous Spatial Pyramid Pooling Integrated Encoder-Decoder Network and Structural Similarity Loss

et al. 2019

View full text Add to dashboard Cite

The technology used for road extraction from remote sensing images plays an important role in urban planning, traffic management, navigation, and other geographic applications. Although deep learning methods have greatly enhanced the development of road extractions in recent years, this technology is still in its infancy. Because the characteristics of road targets are complex, the accuracy of road extractions is still limited. In addition, the ambiguous prediction of semantic segmentation methods also makes the road extraction result blurry. In this study, we improved the performance of the road extraction network by integrating atrous spatial pyramid pooling (ASPP) with an Encoder-Decoder network. The proposed approach takes advantage of ASPP’s ability to extract multiscale features and the Encoder-Decoder network’s ability to extract detailed features. Therefore, it can achieve accurate and detailed road extraction results. For the first time, we utilized the structural similarity (SSIM) as a loss function for road extraction. Therefore, the ambiguous predictions in the extraction results can be removed, and the image quality of the extracted roads can be improved. The experimental results using the Massachusetts Road dataset show that our method achieves an F1-score of 83.5% and an SSIM of 0.893. Compared with the normal U-net, our method improves the F1-score by 2.6% and the SSIM by 0.18. Therefore, it is demonstrated that the proposed approach can extract roads from remote sensing images more effectively and clearly than the other compared methods.

show abstract

“…We have set the value of both C 1 and C 2 to 0.03L, where L represents the maximum possible intensity level in an image (i.e., for an 8-bit image, L=255). Once the SSIM value for each pixel is obtained following (4), the SSIM Loss (L MS−SSIM ) is computed as follows: (5) In the present work, we have used the Multi-scale SSIM (MS-SSIM) [29] which typically computes SSIM at multiple scales through a process of sub-sampling, and is highly effective in finding a similarity index between any two given images [29,30,31].…”

Section: Ms-ssim Lossmentioning

confidence: 99%

Image Enhancement and Denoising in Extreme Low-Light Conditions

2019

IJITEE

View full text Add to dashboard Cite

 Abstract: Image noise refers to the specks of false colors or artifacts that diminish the visual quality of the captured image. It has become our daily experience that with affordable smart-phone cameras we can capture high clarity photos in a brightly illuminated scene. But using the same camera in a poorly lit environment with high ISO settings results in images that are noisy with irrelevant specks of colors. Noise removal and contrast enhancement in images have been extensively studied by researchers over the past few decades. But most of these techniques fail to perform satisfactorily if the images are captured in an extremely dark environment. In recent years, computer vision researchers have started developing neural network-based algorithms to perform automated de-noising of images captured in a low-light environment. Although these methods are reasonably successful in providing the desired de-noised image, the transformation operation tends to distort the structure of the image contents to a certain extent. We propose an improved algorithm for image enhancement and de-noising using the camera's raw image data by employing a deep U-Net generator. The network is trained in an end-to-end manner on a large training set with suitable loss functions. To preserve the image content structures at a higher resolution compared to the existing approaches, we make use of an edge loss term in addition to PSNR loss and structural similarity loss during the training phase. Qualitative and quantitative results in terms of PSNR and SSIM values emphasize the effectiveness of our approach.

show abstract

Applicability of Existing Objective Metrics of Perceptual Quality for Adaptive Video Streaming

Cited by 33 publications

References 18 publications

Image Quality Assessment through FSIM, SSIM, MSE and PSNR—A Comparative Study

Image Quality Assessment through FSIM, SSIM, MSE and PSNR—A Comparative Study

Road Extraction by Using Atrous Spatial Pyramid Pooling Integrated Encoder-Decoder Network and Structural Similarity Loss

Image Enhancement and Denoising in Extreme Low-Light Conditions

Contact Info

Product

Resources

About