Multi-view Encoding Disparity Vector Computation
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Solution Overview
Problem
Current multi-view image coding technologies lack a method to accurately acquire disparity vectors at the decoding end, which hinders efficient encoding and decoding processes.
Innovation Solution
The method involves encoding location parameter information for cameras and transmitting it to the decoding end, where it is used to determine disparity vectors between views, improving encoding and decoding efficiency by leveraging the relationship between camera locations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If disparity vectors are estimated using conventional methods without location parameter information, then the decoding process can proceed, but the accuracy of disparity vector acquisition is insufficient
Solution Approach 1:
The patent applies preliminary action by encoding and transmitting camera location parameter information before the disparity vector estimation process. The encoder pre-processes and stores location parameters (such as camera positions and orientations) in the bitstream, which are then used by the decoder to guide the disparity vector calculation. This preliminary preparation enables more accurate disparity estimation without requiring complex real-time computations during decoding.
Solution Approach 2:
The patent introduces location parameter information as an intermediary element that mediates between the encoded video data and the disparity vector estimation process. These location parameters serve as auxiliary data that bridge the gap between conventional encoding and accurate disparity vector acquisition, enabling the decoder to compute disparity vectors more precisely by referencing the transmitted camera location information.
2Productivity
If location parameter information is encoded and transmitted to improve disparity vector accuracy, then decoding efficiency is improved, but the bitstream size increases
Solution Approach 1:
The patent applies parameter changes by encoding location parameters in a compressed and optimized format. Rather than transmitting full-resolution camera coordinate data, the system transforms location information into essential parameters (such as relative positions, baseline distances, or simplified coordinate representations) that capture the necessary geometric relationships with reduced data volume. This parameter transformation maintains decoding efficiency while minimizing bitstream overhead.
3Adaptability or versatility
If disparity vectors are computed without using camera location information, then the encoding process is simpler, but the multi-view correlation is not effectively utilized
Solution Approach 1:
The patent applies universality by designing a location parameter encoding structure that serves multiple functions simultaneously. The transmitted location information is used not only for disparity vector estimation but also for other multi-view coding operations such as view synthesis, depth map generation, and inter-view prediction. This multi-functional use of location parameters maximizes the benefit of the additional data while justifying the increased encoding complexity through enhanced overall system adaptability.
Data Source
AI summary
A method of obtaining a disparity vector and its encoding/decoding in the multi-view coding process is disclosed. The present invention includes essentially: determining a disparity vector between two views during the multi-view image coding, and computing a disparity vector between the other two views according to the disparity vector between the two views and the known relative location information between each of the views. Thereafter, the disparity vector is used for multi-view encoding/decoding. The present invention further makes use of the correlation between the disparity vector and depth information of the spatial vector on one hand, and on the other hand makes use of the direct relationship between the disparity vector and the location of each of the cameras. It is experimentally proved that the disparity vector between several views can be accurately computed during the multi-view coding, thereby improving the performance of multi-view coding.