Tree-Structured Block Division for High-Resolution 360° Image Coding
Find Innovative SolutionsGenerate Solutions
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, leading to insufficient performance in encoding and decoding high-resolution images.
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 decoded image based on projection formats like ERP, CMP, OHP, 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 captured and processed for 360-degree images, then the quality and realism of virtual and augmented reality media services are improved, but the amount of data generated increases massively leading to insufficient processing performance
Solution Approach 1:
The image is divided into multiple blocks that are processed independently through the prediction and transformation process. Each block undergoes separate motion compensation and DCT transformation, allowing parallel processing and reducing the computational burden on the overall system, thereby improving processing performance while maintaining image quality
Solution Approach 2:
The prediction signal is extracted and separated from the original image data. By calculating motion compensation predictions and extracting only the residual differences, the system reduces the amount of data that needs to be transmitted and processed, improving processing efficiency without compromising the quality of the reconstructed 360-degree image
2Reliability
If high-resolution images are encoded and decoded, then the quality of virtual and augmented reality media services is improved, but the data volume increases leading to insufficient encoding and decoding performance
Solution Approach 1:
Different processing techniques are applied to different regions of the image based on their characteristics. Motion compensation prediction is applied adaptively to blocks where it is beneficial, while other regions may use different processing. This localized approach optimizes the balance between quality and processing performance for high-resolution images
Solution Approach 2:
The system transforms image data from spatial domain to frequency domain using DCT transformation, changing the representation parameters of the image data. This parameter transformation allows for more efficient compression and processing of high-resolution images, improving encoding and decoding performance while maintaining quality
3Speed
If conventional image processing methods are used, then the processing speed is maintained, but the compression performance is insufficient for handling large data volumes
Solution Approach 1:
Motion compensation prediction is performed in advance on each block before the actual encoding process. By pre-calculating the prediction signals and residuals, the system prepares the data in an optimized format that enables faster subsequent processing and more efficient compression, improving both speed and compression performance
Solution Approach 2:
The system replaces conventional mechanical image processing approaches with mathematical transformations, specifically DCT transformation. This substitution enables more efficient processing of image blocks, achieving better compression ratios without sacrificing processing speed, thus improving overall compression efficiency for large data volumes
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.