Super-Resolution Model for Image Compression Throughput
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Solution Overview
Problem
Existing image and video compression techniques face challenges with slow compression throughput and lower perceptual quality compared to machine learning-based methods, while requiring significant computational resources and storage costs due to the need for multiple image resolutions and manual design of filters.
Innovation Solution
Incorporating deep learning-based image super-resolution into end-to-end compression to enhance compression throughput and reduce file size, allowing for flexible production of target image resolutions and efficient compression across diverse devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional image compression techniques are used, then compression throughput is slow, but computational resources and storage costs are reduced
Solution Approach 1:
A super-resolution model is introduced as an intermediary component between the decoder and the final image output. The decoder first generates a low-resolution reconstructed image, which is then processed by the super-resolution model to enhance it to the target resolution. This intermediary approach allows the compression system to operate at lower resolutions (reducing computational load) while still delivering high-quality images at the desired resolution, thereby improving compression throughput without excessive computational resource consumption.
Solution Approach 2:
The image reconstruction process is segmented into two distinct stages: (1) a decoder that efficiently compresses and reconstructs images at a reduced resolution, and (2) a super-resolution model that enhances the reconstructed image to the target resolution. This segmentation allows each component to be optimized independently - the decoder for speed and compression ratio, and the super-resolution model for quality enhancement - resolving the contradiction between throughput and computational resources.
2Adaptability or versatility
If multiple image resolutions are maintained for diverse devices, then adaptability is improved, but storage costs increase
Solution Approach 1:
The super-resolution model serves as a universal solution that can convert a single low-resolution image into multiple high-resolution versions suitable for different target resolutions and diverse devices. Instead of storing and transmitting multiple preprocessed images at different resolutions, the system stores one compressed image and uses the super-resolution model to adaptively generate the required resolution on-demand, thereby reducing storage costs while maintaining adaptability to diverse devices.
Solution Approach 2:
The system dynamically adjusts the output resolution of the super-resolution model based on the target device requirements. Rather than maintaining static multiple resolutions, the model can be configured to generate images at any desired resolution, providing dynamic adaptability that reduces the need for storing multiple fixed-resolution versions, thus lowering storage costs while maintaining versatility.
3Manufacturing precision
If machine learning-based compression is used, then perceptual quality is improved, but computational resources increase
Solution Approach 1:
The system applies machine learning selectively - using a traditional compression algorithm for the initial compression (which is computationally efficient) and then applying the super-resolution model only to the reconstructed image to enhance quality. This partial application of ML techniques achieves improved perceptual quality without requiring ML-based compression throughout the entire pipeline, thereby controlling computational resource consumption while still delivering high-quality results.
Data Source
AI summary
Techniques for using machine learning (ML) for image processing are disclosed. First encoded image data, generated by encoding a first one or more digital images using an encoder, is received. A first reconstructed one or more digital images are generated by decoding the encoded image data using a decoder corresponding to the encoder. A second reconstructed one or more digital images are generated by transforming the first reconstructed one or more digital images using a super-resolution ML model. The second reconstructed one or more digital images has a higher image resolution compared with the first reconstructed one or more digital images, and the super-resolution ML model is trained based on an image resolution corresponding to at least one of the second reconstructed one or more digital images.