Physics Maths Engineering
HYBRID SCHEMES BASED ON WAVELET TRANSFORM AND CONVOLUTIONAL AUTO-ENCODER FOR IMAGE COMPRESSION
Houda Chakib,
Najlae Idrissi,
Oussama Jannani
Related Subjects
Abstract
In recent years, image compression techniques have received a lot of attention from researchers as the number of images at hand keep growing. Digital Wavelet Transform is one of them that has been utilized in a wide range of applications and has shown its efficiency in image compression field. Moreover, used with other various approaches, this compression technique has proven its ability to compress images at high compression ratios while maintaining good visual image quality. Indeed, works presented in this paper deal with mixture between Deep Learning algorithms and Wavelets Transformation approach that we implement in different color spaces. In fact, we investigate RGB and Luminance/Chrominance YCbCr color spaces to develop three image compression models based on Convolutional Auto-Encoder (CAE). In order to evaluate the models’ performances, we used 24 raw images taken from Kodak database and applied the approaches on every one of them and compared achieved experimental results with those obtained using standard compression method. We draw this comparison in terms of performance parameters: Structural Similarity Index Metrix SSIM, Peak Signal to Noise Ratio PSNR and Mean Square Error MSE. Reached results indicates that with proposed schemes we gain significate improvement in distortion metrics over traditional image compression method especially SSIM parameter and we managed to reduce MSE values over than 50%. In addition, proposed schemes output images with high visual quality where details and textures are clear and distinguishable.
Key Questions
What is the focus of this research on image compression?
This research focuses on improving image compression techniques by combining Deep Learning algorithms, specifically Convolutional Auto-Encoders (CAEs), with Digital Wavelet Transform. The goal is to achieve high compression ratios while maintaining excellent visual quality in compressed images.
What is Digital Wavelet Transform, and why is it used?
Digital Wavelet Transform is a mathematical tool used to decompose images into different frequency components. It is highly effective for image compression because it preserves important details while reducing file size. When combined with Deep Learning, it enhances compression efficiency and image quality.
How does Deep Learning improve image compression?
Deep Learning, specifically Convolutional Auto-Encoders (CAEs), is used to learn and extract important features from images. This allows the system to compress images more effectively while retaining critical details and textures, resulting in higher visual quality compared to traditional methods.
What color spaces were used in this study?
The study explored two color spaces: RGB and Luminance/Chrominance (YCbCr). These color spaces were used to develop three image compression models, each optimized for different aspects of image quality and compression efficiency.
How were the models evaluated?
The models were tested on 24 raw images from the Kodak database. Performance was evaluated using three key metrics:
- Structural Similarity Index Metric (SSIM): Measures the similarity between the original and compressed images.
- Peak Signal-to-Noise Ratio (PSNR): Evaluates the quality of the compressed image relative to the original.
- Mean Square Error (MSE): Quantifies the difference between the original and compressed images.
What were the key findings of the study?
The study found that:
- The proposed models significantly improved distortion metrics, especially SSIM, compared to traditional methods.
- MSE values were reduced by more than 50%, indicating better preservation of image quality.
- Compressed images maintained high visual quality, with clear details and textures.
How does this approach compare to traditional image compression methods?
The proposed approach outperforms traditional methods in terms of both compression efficiency and image quality. It achieves higher SSIM and PSNR values while significantly reducing MSE, ensuring that compressed images retain more detail and clarity.
What are the practical applications of this research?
This research has wide-ranging applications, including:
- Improving image storage and transmission efficiency for websites and social media platforms.
- Enhancing medical imaging systems where high-quality compression is critical.
- Optimizing video streaming services by reducing bandwidth requirements without compromising visual quality.
What are the limitations of the proposed approach?
While the approach shows significant improvements, it may require substantial computational resources for training and implementation. Additionally, the performance may vary depending on the complexity and resolution of the input images.
What are the future directions for this research?
Future research could focus on:
- Extending the approach to video compression for real-time applications.
- Exploring additional color spaces and hybrid models to further improve compression efficiency.
- Reducing computational requirements to make the system more accessible for real-world use.
Why is this research important?
As the number of digital images continues to grow, efficient compression techniques are essential for storage, transmission, and processing. This research advances the field by combining Deep Learning and Wavelet Transform to achieve high-quality compression, making it a valuable contribution to both academia and industry.
