Integral Image Encoding Using Adaptive Multi-View Compression
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Solution Overview
Problem
Current methods for compressing integral images in 3D television, such as using MPEG-4 AVC or 3D-DCT algorithms, fail to optimally exploit spatial and temporal redundancies, leading to inefficient compression and redundant information transmission.
Innovation Solution
The method generates sub-images from elementary images, arranges them according to a predetermined pattern to form a multi-view image, and applies adaptive compression based on object movement, predicting sub-images, calculating motion vectors, and transmitting residual data to reduce coding costs and redundancy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If MPEG-4 AVC or 3D-DCT algorithms are used to compress integral images, then compression is performed, but spatial and temporal redundancies are not optimally exploited leading to inefficient compression
Solution Approach 1:
The patent segments the integral image into multiple sub-images arranged in a specific pattern. This segmentation allows independent processing and optimization of different regions, enabling better exploitation of spatial and temporal redundancies within each segment while maintaining overall compression efficiency.
Solution Approach 2:
The patent introduces a new dimensional organization by arranging sub-images in a specific spatial pattern that exploits the multi-view nature of integral images. This dimensional reorganization enables more effective redundancy removal by considering relationships across multiple views and time frames simultaneously.
2Ease of manufacture
If individual frames are treated as traditional video sequences, then encoding is straightforward, but spatial and temporal redundancies specific to integral images are not optimally exploited
Solution Approach 1:
The patent divides the integral image into multiple sub-images that can be processed independently using standard video coding techniques. This segmentation maintains encoding simplicity while allowing optimization of compression performance through targeted redundancy removal in each segment.
Solution Approach 2:
The patent makes the encoding system multi-functional by combining standard video coding capabilities with integral image-specific processing. The same encoding framework handles both traditional video sequences and integral images, with additional processing steps activated only when needed for integral images.
3Productivity
If sub-images are rearranged to exploit redundancies, then compression improves, but significant redundant information remains untapped particularly the strong correlation between adjacent full-frame images
Solution Approach 1:
The patent introduces a new dimensional organization by arranging sub-images in a specific spatial pattern that exploits the multi-view nature of integral images. This dimensional reorganization enables more effective redundancy removal by considering relationships across multiple views and time frames simultaneously, capturing correlations that single-frame methods miss.
Solution Approach 2:
The patent performs preliminary reorganization of sub-images into an optimized pattern before applying compression algorithms. This preliminary arrangement pre-positions related data elements to maximize the effectiveness of subsequent redundancy removal operations, enabling better exploitation of temporal and spatial correlations.
4Productivity
If adaptive compression is applied based on object movement, then coding costs are reduced, but complexity increases in predicting and calculating motion vectors
Solution Approach 1:
The patent applies adaptive compression selectively based on detected motion characteristics. Instead of applying complex prediction and calculation to all sub-images uniformly, the system performs these operations only where and when motion is detected, reducing overall computational complexity while maintaining coding efficiency benefits where needed.
Solution Approach 2:
The patent performs preliminary motion detection and classification before applying full adaptive compression processing. By identifying regions with significant motion early in the process, the system can prepare appropriate processing strategies in advance, reducing real-time computational complexity while maintaining compression effectiveness.
Data Source
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AI summary
The invention relates to the encoding of at least one integral image (InI) representing at least one object in a scene in perspective, and including a plurality of basic images, said encoding involving a step of generating a plurality of K sub-images (SI1, SI2, ..., SIK) from said plurality of basic images. Such an encoding method is characterised in that it involves the steps of: arranging (C3) said sub-images according to a predetermined pattern so as to form a multi-view image of said object, said views corresponding to said sub-images, respectively; and adaptively compressing (C4) the formed multi-view image according to the type of movement of the object in the scene.