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Updated: Jul 7, 2026
11:23
Lensless Fluorescent Microscopy on a Chip
Published on: August 17, 2011
Lossy compression of noisy images
1Sch. of Electr. and Comput. Eng., Georgia Inst. of Technol., Atlanta, GA 30332, USA. osamakl@eecs.berkeley.edu
Summary
This study investigates how noise impacts lossy image compression. Preprocessing images to remove noise before compression significantly improves image quality at lower bit rates.
Area of Science:
- Digital image processing
- Information theory
- Signal processing
Background:
- Noise significantly degrades image compression performance.
- Common noise types include Gaussian, Poisson, and film-grain noise.
- Lossy compression algorithms are sensitive to input data quality.
Purpose of the Study:
- To analyze the impact of various noise types on lossy image compression.
- To develop an optimal compression strategy for noisy images.
- To evaluate the effectiveness of noise preprocessing on compression efficiency.
Main Methods:
- Investigated the effects of Gaussian, Poisson, and film-grain noise on image compression.
- Measured distortion relative to the original image, not the compressed input.
- Derived minimum-mean-square-error (MMSE) estimators and coders for noisy image compression.
- Evaluated preprocessing using standard coders like JPEG.
Main Results:
- Noise adversely affects image compression performance.
- A preprocessing step (MMSE estimator) followed by an MMSE coder optimizes compression for noisy images.
- This approach yields higher image quality at reduced bit rates.
- Standard coders also benefit from noise preprocessing.
Conclusions:
- Noise reduction preprocessing is crucial for effective lossy image compression.
- The proposed MMSE-based approach enhances compression efficiency and output quality.
- This method is applicable to various noise types and standard compression algorithms.
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