Deep Architectures for Image Compression: A Critical Review

Mishra, Dipti; Singh, Satish Kumar; Singh, Rajat Kumar

doi:10.1016/j.sigpro.2021.108346

Cited by 69 publications

(16 citation statements)

References 71 publications

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“…It was found that decreasing the JPEG quality parameter resulted in decreased image quality and vice versa but only up to a certain point (75-80 image quality index), where further increasing the quality parameter did not significantly improve image quality. This is consistent with previous studies that have shown a trade-off between file size and image quality with JPEG compression [26]. Interestingly, we found that the mean and median temperature values of the ROIs were not significantly affected by the decrease in JPEG quality, except for the largest ROI, where the mean temperature value decreased slightly at higher compression rates.…”

Section: Discussionsupporting

confidence: 92%

The Dangers of Analyzing Thermographic Radiometric Data as Images

et al. 2023

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Thermography is probably the most used method of measuring surface temperature by analyzing radiation in the infrared part of the spectrum which accuracy depends on factors such as emissivity and reflected radiation. Contrary to popular belief that thermographic images represent temperature maps, they are actually thermal radiation converted into an image, and if not properly calibrated, they show incorrect temperatures. The objective of this study is to analyze commonly used image processing techniques and their impact on radiometric data in thermography. In particular, the extent to which a thermograph can be considered as an image and how image processing affects radiometric data. Three analyzes are presented in the paper. The first one examines how image processing techniques, such as contrast and brightness, affect physical reality and its representation in thermographic imaging. The second analysis examines the effects of JPEG compression on radiometric data and how degradation of the data varies with the compression parameters. The third analysis aims to determine the optimal resolution increase required to minimize the effects of compression on the radiometric data. The output from an IR camera in CSV format was used for these analyses, and compared to images from the manufacturer’s software. The IR camera providing data in JPEG format was used, and the data included thermographic images, visible images, and a matrix of thermal radiation data. The study was verified with a reference blackbody radiation set at 60 °C. The results highlight the dangers of interpreting thermographic images as temperature maps without considering the underlying radiometric data which can be affected by image processing and compression. The paper concludes with the importance of accurate and precise thermographic analysis for reliable temperature measurement.

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Section: Discussionsupporting

confidence: 92%

The Dangers of Analyzing Thermographic Radiometric Data as Images

et al. 2023

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“…In recent years, learning-based image compression has been a major focus of research. For lossy image compression based on learning, different CNN-based architectures have proven effective [85,86]. As ViTs evolved, learning-based image compression has also been performed using transformer-based models.…”

Section: Vits For Image Compressionmentioning

confidence: 99%

A Comprehensive Survey of Transformers for Computer Vision

Jamil

Piran

Kwon

2023

Drones

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As a special type of transformer, vision transformers (ViTs) can be used for various computer vision (CV) applications. Convolutional neural networks (CNNs) have several potential problems that can be resolved with ViTs. For image coding tasks such as compression, super-resolution, segmentation, and denoising, different variants of ViTs are used. In our survey, we determined the many CV applications to which ViTs are applicable. CV applications reviewed included image classification, object detection, image segmentation, image compression, image super-resolution, image denoising, anomaly detection, and drone imagery. We reviewed the state of the-art and compiled a list of available models and discussed the pros and cons of each model.

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“…DNN-based approaches for image compression are proven to be very successful. Autoencoder comes under this approach (11,12) . https://www.indjst.org/…”

Section: Image Compression Using Machine Learning (Ml)mentioning

confidence: 99%

A Novel Image Compression Algorithm Based on Autoencoder for Biometric Template Protection Scheme

Vasuki¹,

Prasanna²

2023

IJST

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Objectives: To evaluate the proposed deep learning-based method for biometric template protection schemes with other existing image compression methods. Methods: In this study, four image compression methods such as JPEG_LS, Vector quantization (VQ), Run-length encoding (RLE), and the Autoencoder methods are implemented. The experimental results are compared with the different performance parameters that are applied over the CASIA I iris dataset possessing 756 images. Findings: The proposed autoencoder method algorithm offered enhanced results in terms of PSNR, SSIM, MSE, and CR when compared to other transform methods. The deep neural network-based autoencoder algorithm achieved the highest compression ratio of 89.05 percent, while the conventional algorithms achieved the highest image quality rate of 94.05 percent. Novelty: A novel autoencoder-based image compression model has been proposed in this study. The proposed Autoencoder (AE) method incorporates five stages like Initialization, Learning compression representations, Generation of chaotic sequence, Image encryption and decryption, and Image reconstruction. The generation of the chaotic sequence using the stochastic logistic map, as well as the learning compression representations contributes towards the novelty of this study.

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Deep Architectures for Image Compression: A Critical Review

Cited by 69 publications

References 71 publications

The Dangers of Analyzing Thermographic Radiometric Data as Images

The Dangers of Analyzing Thermographic Radiometric Data as Images

A Comprehensive Survey of Transformers for Computer Vision

A Novel Image Compression Algorithm Based on Autoencoder for Biometric Template Protection Scheme

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