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Related Experiment Video

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Deep Learning-Based Segmentation of Cryo-Electron Tomograms

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Deep Learning based Picture-Wise Just Noticeable Distortion Prediction Model for Image Compression.

Huanhua Liu, Yun Zhang, Huan Zhang

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society

|August 20, 2019

Summary

This study introduces a deep learning model for predicting Picture Wise Just Noticeable Difference (PW-JND) in image compression. The novel approach improves accuracy over conventional methods for perception-oriented image processing.

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Area of Science:

Computer Vision
Image Processing
Machine Learning

Background:

Conventional Just Noticeable Difference (JND) models often calculate thresholds pixel-by-pixel, potentially missing overall image masking effects.
Accurate JND prediction is crucial for perception-oriented image and video processing applications.

Purpose of the Study:

To develop a deep learning-based model for predicting Picture Wise Just Noticeable Difference (PW-JND) in image compression.
To address the limitations of conventional JND models in capturing comprehensive masking effects.

Main Methods:

Formulated PW-JND prediction as a multi-class classification problem, transformed into a binary classification task.
Developed a deep learning binary classifier, the perceptually lossy/lossless predictor.
Implemented a sliding window search strategy utilizing the predictor's results for PW-JND prediction.

Main Results:

The perceptually lossy/lossless predictor achieved a mean accuracy of 92%.
The proposed PW-JND model demonstrated an average absolute prediction error of 0.79 dB.
The deep learning approach showed superiority compared to conventional JND models.

Conclusions:

The proposed deep learning model offers a more accurate and efficient method for PW-JND prediction in image compression.
This advancement can enhance the quality and efficiency of perception-oriented image processing techniques.