Lossless Image Compression via Differential Tile Transfers
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Solution Overview
Problem
The increasing resolution of images in electronic devices leads to a significant memory footprint, causing bandwidth and memory constraints, especially in devices like mobile phones, which struggle to process and store high-resolution images efficiently, and existing compression methods can be prohibitive in terms of time and hardware requirements.
Innovation Solution
Implementing lossless image compression using differential transfers, where image data is initially transferred using lossy compression due to resource limitations, but subsequent frames provide additional data to generate a complete, lossless image, taking advantage of unchanged image portions to reduce bandwidth pressure and memory competition.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If lossless compression is applied to high-resolution images, then image quality is preserved, but processing time and hardware requirements increase
Solution Approach 1:
The patent divides the image into multiple tiles and processes them independently. Each tile can be compressed and transmitted separately, allowing parallel processing and reducing the overall time required compared to processing the entire high-resolution image at once. This segmentation enables the system to achieve lossless compression while managing processing time through distributed computation.
Solution Approach 2:
The patent applies preliminary compression to individual tiles before final assembly. By pre-compressing each tile using lossless algorithms and storing them in a compressed format, the system prepares image data in advance for subsequent transmission or display. This preliminary action reduces the computational burden during actual image rendering or transmission, effectively managing processing time while maintaining image quality.
2Measurement precision
If image resolution is increased, then image quality improves, but memory footprint increases
Solution Approach 1:
The patent segments the high-resolution image into smaller tiles that can be processed and stored independently. Each tile occupies a portion of the memory footprint, allowing the system to manage memory usage more efficiently. The segmented approach enables selective processing and storage of only the necessary portions of the image, reducing overall memory requirements while maintaining high image quality when needed.
Solution Approach 2:
The patent changes the storage parameters by using compressed data formats for image tiles. By applying lossless compression algorithms, the system reduces the amount of data required to represent each tile while maintaining image quality. This parameter change in data representation allows the system to store high-resolution images with reduced memory footprint, effectively resolving the contradiction between image quality and memory consumption.
3Loss of information
If bandwidth is limited, then data transmission is constrained, but lossless compression cannot be achieved
Solution Approach 1:
The patent segments the image data into tiles that can be transmitted in smaller chunks over the bandwidth-limited channel. By dividing the large image into manageable tile units, the system can transmit compressed tile data more efficiently within bandwidth constraints. The segmented transmission allows for progressive loading and processing, achieving lossless compression while adapting to limited bandwidth conditions through distributed data transfer.
Solution Approach 2:
The patent applies preliminary lossless compression to image tiles before transmission over the bandwidth-limited channel. By pre-compressing the tile data using efficient algorithms, the system reduces the amount of data that needs to be transmitted while maintaining information completeness. This preliminary compression action enables the system to achieve lossless transmission within bandwidth constraints by minimizing the data volume that traverses the limited channel.
Data Source
AI summary
Lossless image compression using differential transfers may involve an image compression unit receiving image data for an image in a sequence of images and transmitting the image data such that image data for at least some image tiles is transmitted using lossy compression due to resource limitations. The image compression unit may then receive image data for a subsequent image in the sequence and determine that the image data for at least some tiles does not change relative to the image data for corresponding tiles of the previous image. The image compression unit may then transmit image data in a manner sufficient to create lossless versions of tiles for which lossily compressed image data was sent previously.