Introduction:

In today’s digital age, the demand for efficient image compression techniques is on the rise. Whether it’s for quick transmission over networks or the need to save storage space, image compression plays a vital role. In this article, we’ll explore a novel approach that combines two compression methods — image resizing and binary compression — with a low-latency recovery solution known as EfficientSRGAN (Efficient Super-Resolution Generative Adversarial Network).

Compression Methods:

1. Image Resizing:
One of the primary methods used to compress images is resizing. In our approach, we resize the original image to one-fourth of its original size. This step results in a significant reduction in image dimensions, which forms the initial stage of our compression process.

2. Shrink Binary:
— Convert 8-bit Pixel Image to 4-bit Pixel Image: This step reduces the color depth of the image, transforming each pixel from an 8-bit representation to a 4-bit representation.
— Split Image into Top and Bottom: After the conversion, we split the image into two equal parts, the top, and the bottom.
— Combine Top and Bottom Images: The top and bottom images are recombined to produce an 8-bit image. This innovative technique results in a compressed image while preserving important visual information.

Restoration:

1. Binary Restoration Inverse Method of Shrink Binary:
To restore the image, we apply an inverse method to the shrink binary process. This method transforms the 4-bit pixel image back to an 8-bit image, recovering the lost color information.

2. EfficientSRGAN for Color Restoration:
— Super-Resolution GAN with EfficientNet Backbone: We employ a Super-Resolution Generative Adversarial Network (SRGAN) that utilizes EfficientNet as its backbone architecture. This powerful combination allows us to achieve high-quality image restoration.
—Training with Color Degradation from Binary Restoration: During the training process, we preprocess input images using the color degradation from the binary restoration step. This approach enables the network to learn how to restore color efficiently and accurately.

Results:

1. Image Size Reduction: The combined effect of image resizing and binary compression results in an impressive 32 times reduction in the size of the original image. This level of compression can significantly benefit applications with strict storage and bandwidth limitations.

2. Model Performance Metrics:
— SRGAN:
— — Parameters: 1.549 million
— — GFlops (Giga-Floating Point Operations per Second): 9.128
— EfficientSRGAN:
— — Parameters: 0.319 million
— — GFlops: 2.102
These metrics demonstrate the efficiency of the EfficientSRGAN model, which not only achieves high-quality image restoration but also boasts significantly fewer parameters and lower computational requirements compared to traditional SRGANs.

Conclusion:

Efficient image compression and low-latency recovery are crucial in various applications, from efficient data transmission to enhancing user experiences. The combination of image resizing and binary compression, along with the powerful EfficientSRGAN model, offers a compelling solution. With substantial image size reduction and impressive performance metrics, this approach is poised to make a significant impact in the field of image compression and restoration, opening up new possibilities for a wide range of applications.