IMAGE COMPRESSION AUGMENTED WITH A LEARNING-BASED SUPER RESOLUTION MODEL

Smart overview of the Invention

Recent advancements in image processing leverage machine learning (ML) to enhance image compression methods. The process begins with receiving encoded image data derived from one or more digital images using an encoder. Following this, a decoder reconstructs the original images, which are then further improved in resolution through a super-resolution ML model.

Super-Resolution Model Functionality

The super-resolution model transforms the initially reconstructed images into a higher resolution version. This transformation is significant as it enhances the visual quality of the images while maintaining efficiency in data encoding and decoding. The model is specifically trained to optimize the resolution for these enhanced images, ensuring superior output compared to traditional methods.

Challenges in Existing Compression Techniques

Current image and video compression techniques face challenges such as slow processing speeds and lower quality outputs. Traditional methods like JPEG and HEVC are computationally efficient but often result in lower perceptual quality at similar bitrates compared to ML-based techniques. Additionally, these conventional methods require extensive manual design efforts, adding complexity and cost to deployment.

Advantages of the Proposed Techniques

The described techniques address the limitations of prior methods by integrating deep learning into the compression process. This integration allows for faster throughput, reduced file sizes, and improved image quality, making it suitable for various applications like social media and video streaming. The system can also adapt to different display resolutions, optimizing performance across diverse devices.

Implementation and Future Applications

By utilizing a combination of down-sampling techniques and learned transformations, the proposed method streamlines image compression while enhancing overall quality. This approach not only saves on storage costs but also simplifies processing requirements by eliminating the need for multiple resolutions. As demand for high-quality images continues to grow, these techniques are poised to play a critical role in future image processing applications.