A Fast Method for Visual Quality Prediction and Providing in Image Lossy Compression by SPIHT

2021 ◽  
pp. 17-29
Author(s):  
Fangfang Li ◽  
Sergey Krivenko ◽  
Vladimir Lukin
Keyword(s):  
Visual Quality ◽  
Lossy Compression ◽  
Fast Method ◽  
Quality Prediction

scholarly journals Estimation of distortion sensitivity for visual quality prediction using a convolutional neural network

2019 ◽  
Vol 91 ◽  
pp. 54-65 ◽  
Author(s):  
Sebastian Bosse ◽  
Sören Becker ◽  
Klaus-Robert Müller ◽  
Wojciech Samek ◽  
Thomas Wiegand
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Visual Quality ◽  
Quality Prediction

scholarly journals Lossy Compression of Noisy Images Based on Visual Quality: A Comprehensive Study

2010 ◽  
Vol 2010 (1) ◽  
Author(s):  
Nikolay Ponomarenko ◽  
Sergey Krivenko ◽  
Vladimir Lukin ◽  
Karen Egiazarian ◽  
Jaakko T. Astola
Keyword(s):  
Visual Quality ◽  
Lossy Compression ◽  
Noisy Images ◽  
Comprehensive Study

scholarly journals TWO-STEP PROVIDING OF DESIRED QUALITY IN LOSSY IMAGE COMPRESSION BY SPIHT

2020 ◽  
pp. 22-32
Author(s):  
Fangfang Li ◽  
Sergey Krivenko ◽  
Vladimir Lukin
Keyword(s):  
Image Compression ◽  
Visual Quality ◽  
Lossy Compression ◽  
Single Step ◽  
Set Partitioning ◽  
Discrete Wavelet ◽  
Compression Method ◽  
Step Method ◽  
Application Range ◽  
Special Cases

Image information technology has become an important perception technology considering the task of providing lossy image compression with the desired quality using certain encoders Recent researches have shown that the use of a two-step method can perform the compression in a very simple manner and with reduced compression time under the premise of providing a desired visual quality accuracy. However, different encoders have different compression algorithms. These issues involve providing the accuracy of the desired quality. This paper considers the application of the two-step method in an encoder based on a discrete wavelet transform (DWT). In the experiment, bits per pixel (BPP) is used as the control parameter to vary and predict the compressed image quality, and three visual quality evaluation metrics (PSNR, PSNR-HVS, PSNR-HVS-M) are analyzed. In special cases, the two-step method is allowed to be modified. This modification relates to the cases when images subject to lossy compression are either too simple or too complex and linear approximation of dependences is no more valid. Experimental data prove that, compared with the single-step method, after performing the two-step compression method, the mean square error of differences between desired and provided values drops by an order of magnitude. For PSNR-HVS-M, the error of the two-step method does not exceed 3.6 dB. The experiment has been conducted for Set Partitioning in Hierarchical Trees (SPIHT), a typical image encoder based on DWT, but it can be expected that the proposed method applies to other DWT-based image compression techniques. The results show that the application range of the two-step lossy compression method has been expanded. It is not only suitable for encoders based on discrete cosine transform (DCT) but also works well for DWT-based encoders.

Still image/video frame lossy compression providing a desired visual quality

2015 ◽  
Vol 27 (3) ◽  
pp. 697-718 ◽  
Author(s):  
Alexander Zemliachenko ◽  
Vladimir Lukin ◽  
Nikolay Ponomarenko ◽  
Karen Egiazarian ◽  
Jaakko Astola
Keyword(s):  
Visual Quality ◽  
Lossy Compression ◽  
Video Frame ◽  
Still Image

scholarly journals A Fast Method of Visually Lossless Compression of Dental Images

2020 ◽  
Vol 11 (1) ◽  
pp. 135
Author(s):  
Sergey Krivenko ◽  
Vladimir Lukin ◽  
Olha Krylova ◽  
Liudmyla Kryvenko ◽  
Karen Egiazarian
Keyword(s):  
Signal To Noise Ratio ◽  
Lossless Compression ◽  
Lossy Compression ◽  
Fast Method ◽  
Signal To Noise ◽  
Quantization Step ◽  
Partition Scheme ◽  
Compressed Images ◽  
Image Coder ◽  
Noise Ratio

A noniterative approach to the problem of visually lossless compression of dental images is proposed for an image coder based on the discrete cosine transform (DCT) and partition scheme optimization. This approach considers the following peculiarities of the problem. It is necessary to carry out lossy compression of dental images to achieve large compression ratios (CRs). Since dental images are viewed and analyzed by specialists, it is important to preserve useful diagnostic information preventing appearance of any visible artifacts due to lossy compression. At last, dental images may contain noise having complex statistical and spectral properties. In this paper, we have analyzed and utilized dependences of three quality metrics (Peak signal-to-noise ratio, PSNR; eak Signal-to-Noise Ratio using Human Visual System and Masking (PSNR-HVS-M); and feature similarity, FSIM) on the quantization step (QS), which controls a compression ratio for the so-called advanced DCT coder (ADCTC). The threshold values of distortion visibility for these metrics have been considered. Finally, the recent results on detectable changes in noise intensity have been incorporated in the QS setting. A visual comparison of original and compressed images allows to conclude that the introduced distortions are practically undetectable for the proposed approach; meanwhile, the provided CR lies within the interval.

Multi-layer fusion network for blind stereoscopic 3D visual quality prediction

2021 ◽  
Vol 91 ◽  
pp. 116095
Author(s):  
Wujie Zhou ◽  
Xinyang Lin ◽  
Xi Zhou ◽  
Jingsheng Lei ◽  
Lu Yu ◽  
...  
Keyword(s):  
Visual Quality ◽  
Quality Prediction ◽  
Stereoscopic 3D

scholarly journals Quality Prediction of DWT-Based Compression for Remote Sensing Image Using Multiscale and Multilevel Differences Assessment Metric

2014 ◽  
Vol 2014 ◽  
pp. 1-15 ◽  
Author(s):  
Hongxu Jiang ◽  
Kai Yang ◽  
Tingshan Liu ◽  
Yongfei Zhang
Keyword(s):  
Remote Sensing ◽  
Prediction Model ◽  
Image Quality Assessment ◽  
Quality Criteria ◽  
Visual Quality ◽  
Remote Sensing Image ◽  
Subjective Perception ◽  
Quality Prediction ◽  
Compressed Images ◽  
Multiscale Sampling

Accurate assessment and prediction of visual quality are of fundamental importance to lossy compression of remote sensing image, since it is not only a basic indicator of coding performance, but also an important guide to optimize the coding procedure. In the paper, a novel quality prediction model based on multiscale and multilevel distortion (MSMLD) assessment metric is preferred for DWT-based coding of remote sensing image. Firstly, we propose an image quality assessment metric named MSMLD, which assesses quality by calculating distortions in three levels and multiscale sampling between original images and compressed images. The MSMLD method not only has a better consistency with subjective perception values, but also shows the distortion features and visual quality of compressed image well. Secondly, some significant characteristics in spatial and wavelet domain that link well with quality criteria of MSMLD are chosen with multiple linear regression and used to establish a compression quality prediction model of MSMLD. Finally, the quality prediction model is extended to a wider range of compression ratios from 4 : 1 to 20 : 1 and tested with experiment. The experimental results show that the prediction accuracy of the proposed model is up to 98.33%, and its mean prediction error is less than state-of-the-art methods.

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