Tree-Structured Block Division for 360-Degree Image Compression
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Solution Overview
Problem
Existing image processing systems struggle with the massive data generated by multi-view images for 360-degree images in virtual and augmented reality, necessitating improved performance in image encoding and decoding.
Innovation Solution
A method for encoding and decoding 360-degree images that includes generating a predicted image using syntax information, combining it with a residual image, and reconstructing the image based on projection formats like ERP, CMP, and ISP, with image expansion and rearrangement of blocks to enhance compression performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multi-view images are processed for 360-degree images, then the realism and quality of virtual reality and augmented reality services are improved, but the amount of data generated increases massively and the processing performance becomes insufficient
Solution Approach 1:
The image is divided into multiple blocks that are processed independently through tree structure-based block division. Each block can be encoded and decoded separately, allowing parallel processing and improving overall processing performance while maintaining image quality.
Solution Approach 2:
The patent introduces a tree structure dimension for block division, transforming the traditional flat block processing into a hierarchical structure. This enables multi-level processing where blocks are divided into sub-blocks recursively, improving processing efficiency through structured organization.
2Productivity
If the amount of data for 360-degree images is reduced through compression, then the processing efficiency is improved, but the image quality and realism may be degraded
Solution Approach 1:
Different blocks and sub-blocks can have different encoding parameters and compression levels applied locally. Important regions can maintain higher quality while less critical areas use higher compression, optimizing the balance between overall image quality and processing efficiency.
Solution Approach 2:
The patent performs preliminary block division and organization into tree structures before encoding. This preliminary structuring allows for more efficient compression by identifying patterns and redundancies early in the process, improving compression ratios without sacrificing image quality.
3Device complexity
If traditional block-based encoding is used, then the processing is simple, but the compression performance is insufficient for large data volumes
Solution Approach 1:
The patent implements nested block division where blocks are divided into sub-blocks, which can be further divided into smaller sub-blocks, creating a nested hierarchical structure. This nested approach enables progressive compression and more efficient data representation, reducing the overall data volume while maintaining encoding manageability.
Solution Approach 2:
The tree structure-based block division allows dynamic adaptation of block sizes and division levels based on image content characteristics. The encoding process can dynamically adjust the granularity of block division to optimize compression performance for different regions of the image.
Data Source
AI summary
Disclosed are methods and apparatuses for image data encoding/decoding. A method of decoding an image includes receiving a bitstream in which the image is encoded; obtaining index information for specifying a block division type of a current block in the image; and determining the block division type of the current block from a candidate group pre-defined in the decoding apparatus. The candidate group includes a plurality of candidate division types, including at least one of a non-division, a first quad-division, a second quad-division, a binary-division or a triple-division. The method also includes dividing the current block into a plurality of sub-blocks; and decoding each of the sub-blocks with reference to syntax information obtained from the bitstream.