Optimized-SSIM Based Quantization in Optical Remote Sensing Image Compression

Yang, Kai; Jiang, Hongxu

doi:10.1109/icig.2011.38

Cited by 20 publications

(16 citation statements)

References 6 publications

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“…Today, IQA research has emerged as an active subdiscipline of image processing, and many of the resulting techniques and algorithms have begun to benefit a wide variety of applications. Variations of IQA algorithms have proved useful for applications such as image and video coding (e.g., [1][2][3]), digital watermarking (e.g., [4][5][6][7][8]), unequal error protection (e.g., [9]), denoising (e.g., [10]), image synthesis (e.g., [11,12]), and various other areas (e.g., for predicting intelligibility in sign language video [13]).…”

Section: Introductionmentioning

confidence: 99%

“…Many of the techniques employed by modern IQA algorithms are founded in the early research on quality evaluation 2 ISRN Signal Processing of optical systems and analog television broadcast and display systems (e.g., [14][15][16][17][18][19][20][21][22][23][24][25]). For example, in their 1940 paper titled "Quality in Television Pictures, " Goldmark and Dyer [16] stated that "The factors which chiefly determine the quality of a television picture are (1) definition, (2) contrast range, (3) gradation, (4) brilliance, (5) flicker, (6) geometric distortion, (7) size, (8) color, and (9) noise. " [16].…”

Section: Introductionmentioning

confidence: 99%

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Seven Challenges in Image Quality Assessment: Past, Present, and Future Research

Chandler

2013

ISRN Signal Processing

374

191

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Image quality assessment (IQA) has been a topic of intense research over the last several decades. With each year comes an increasing number of new IQA algorithms, extensions of existing IQA algorithms, and applications of IQA to other disciplines. In this article, I first provide an up-to-date review of research in IQA, and then I highlight several open challenges in this field. The first half of this article provides discuss key properties of visual perception, image quality databases, existing full-reference, no-reference, and reduced-reference IQA algorithms. Yet, despite the remarkable progress that has been made in IQA, many fundamental challenges remain largely unsolved. The second half of this article highlights some of these challenges. I specifically discuss challenges related to lack of complete perceptual models for: natural images, compound and suprathreshold distortions, and multiple distortions, and the interactive effects of these distortions on the images. I also discuss challenges related to IQA of images containing nontraditional, and I discuss challenges related to the computational efficiency. The goal of this article is not only to help practitioners and researchers keep abreast of the recent advances in IQA, but to also raise awareness of the key limitations of current IQA knowledge.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Seven Challenges in Image Quality Assessment: Past, Present, and Future Research

Chandler

2013

ISRN Signal Processing

374

191

View full text Add to dashboard Cite

show abstract

“…In addition to the custom loss functions for image comparison discussed so far we discuss here a built-in function in TF that is well known in the remote sensing literature [31,32], but not in the meteorological literature. The Structural Similarity Index Measure (SSIM), developed by Wang et al [33], is one of the most common measures in the computer vision literature to measure the similarity of two images.…”

Section: Structural Similarity Index Measure (Ssim)mentioning

confidence: 99%

CIRA Guide to Custom Loss Functions for Neural Networks in Environmental Sciences -- Version 1

Ebert‐Uphoff¹,

Lagerquist²,

Hilburn³

et al. 2021

Preprint

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Neural networks are increasingly used in environmental science applications. Furthermore, neural network models are trained by minimizing a loss function, and it is crucial to choose the loss function very carefully for environmental science applications, as it determines what exactly is being optimized. Standard loss functions do not cover all the needs of the environmental sciences, which makes it important for scientists to be able to develop their own custom loss functions so that they can implement many of the classic performance measures already developed in environmental science, including measures developed for spatial model verification. However, there are very few resources available that cover the basics of custom loss function development comprehensively, and to the best of our knowledge none that focus on the needs of environmental scientists. This document seeks to fill this gap by providing a guide on how to write custom loss functions targeted toward environmental science applications. Topics include the basics of writing custom loss functions, common pitfalls, functions to use in loss functions, examples such as fractions skill score as loss function, how to incorporate physical constraints, discrete and soft discretization, and concepts such as focal, robust, and adaptive loss. While examples are currently provided in this guide for Python with Keras and the TensorFlow backend, the basic concepts also apply to other environments, such as Python with PyTorch. Similarly, while the sample loss functions provided here are from meteorology, these are just examples of how to create custom loss functions. Other fields in the environmental sciences have very similar needs for custom loss functions, e.g., for evaluating spatial forecasts effectively, and the concepts discussed here can be applied there as well. All code samples are provided in a GitHub repository.

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“…At current, IQA study has appear as an energetic sub restraint of image dealing out, and a lot of the ensuing techniques and process encompass begin to assistance a ample range of implementations. Disparity of IQA techniques comprise proved functional for processing such as image and video coding (e.g., [2][3][4]), digital watermarking (e.g., [5][6][7][8][9]), uneven error guard (e.g., [10]), denoising (e.g., [11]), image amalgamation (e.g., [12,13]), and different further areas (e.g., for envisage clearness in signal words tape [14]). This paper has organized as the section 2 shows associated study with the image quality assessment made.…”

Section: Vol 3 (2019) No 3 E-issn : 2549-9904 Issn : 2549-9610mentioning

confidence: 99%

A narrative move towards the exploration of gauging of image quality

Kodi¹,

prasad²,

Kiran³

et al. 2019

JOIV : Int. J. Inform. Visualization

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Image quality assessment (IQA) acting as a noteworthy part in a variety of image processing applications. Manipulative eminence of an image is essential predicament in image and record handling and a range of procedure have been anticipated for IQA.widespread psychological substantiation shows that humans favor to conduct evaluations qualitatively comparative than numerical. However most frequently used IQA metrics are not reliable fine with the individual judgments of image quality. For the majority of the applications, the perceptual momentous compute is the one which can routinely estimate the worth of images or videos involving reliable behavior. This article explains about the various methods and their behavior towards the assessment of image quality.

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Optimized-SSIM Based Quantization in Optical Remote Sensing Image Compression

Cited by 20 publications

References 6 publications

Seven Challenges in Image Quality Assessment: Past, Present, and Future Research

Seven Challenges in Image Quality Assessment: Past, Present, and Future Research

CIRA Guide to Custom Loss Functions for Neural Networks in Environmental Sciences -- Version 1

A narrative move towards the exploration of gauging of image quality

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