Image Compression Tree Using Similar Frame References
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing image compression methods either degrade image quality through lossy compression or fail to significantly reduce storage space with lossless compression, posing challenges in efficiently storing large numbers of images on servers without compromising image quality.
Innovation Solution
A computer-implemented method that compresses images by referencing and sharing data with similar images stored on a server, using intra-frames (I-Frames), predicted frames (P-Frames), and bidirectional predicted frames (B-Frames, to reduce storage space without degrading image quality, by determining similarity thresholds and generating frames that meet specific threshold levels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If lossy compression is used to reduce storage space, then storage space is reduced, but image quality is degraded
Solution Approach 1:
The patent creates compressed representations (P-Frames and B-Frames) that copy only the differences or predictions from reference frames (I-Frames), rather than storing complete images. This allows significant storage reduction while maintaining the ability to reconstruct full-quality images when needed.
Solution Approach 2:
The patent implements a hierarchical frame structure where P-Frames are nested within I-Frames, and B-Frames are nested within P-Frames. Each frame type contains references to previous frames, creating a nested data structure that enables progressive reconstruction of full image quality from compressed data.
2Manufacturing precision
If lossless compression is used to maintain image quality, then image quality is preserved, but storage space is not significantly reduced
Solution Approach 1:
The patent merges multiple reference frames (I-Frames and P-Frames) to create B-Frames that utilize information from both past and future frames. This combining of reference data allows for more efficient compression ratios while maintaining lossless reconstruction capability.
Solution Approach 2:
The patent implements dynamic frame selection and adaptive compression where the system determines whether to use I-Frames, P-Frames, or B-Frames based on image similarity thresholds and content characteristics. This dynamic approach optimizes storage efficiency while preserving image quality.
3Quantity of substance
If similar images share data to reduce storage, then storage space is reduced, but system complexity increases
Solution Approach 1:
The patent segments images into discrete frame types (I-Frames, P-Frames, B-Frames) with specific functions. I-Frames contain complete image data, P-Frames contain predictions from previous I-Frames, and B-Frames contain predictions from both past and future frames. This segmentation simplifies the compression logic while achieving significant storage reduction.
Solution Approach 2:
The patent changes the parameter of frame reference complexity by establishing clear hierarchical relationships: I-Frames reference no other frames, P-Frames reference one previous I-Frame, and B-Frames reference two P-Frames. These defined parameter changes create a manageable complexity structure.
Data Source
AI summary
A method, system, and computer program product for compressing an image using similar images includes: receiving a first image; storing the first image on a storage server; comparing the first image to one or more stored intra-frames (I-Frames) to determine a similar I-Frame from the one or more stored I-Frames; in response to determining the similar I-Frame, determining that one or more stored predicted frames (P-Frames) reference the similar I-Frame; comparing the first image to the one or more stored P-Frames to determine a similar P-Frame; determining whether the first image meets a P-Frame threshold level for the similar P-Frame; in response to determining that the first image meets the P-Frame threshold level, generating a first P-Frame for the first image using data from the similar P-Frame and data from the similar I-Frame to compress storage space used by the first image on the storage server.