Tree-Structured Block Division for 360-Degree Image Decoding
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing image processing systems struggle with the massive data generated for 360-degree images in virtual and augmented reality, necessitating improved performance in image encoding and decoding methods.
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 based on partitioning units.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If conventional image encoding methods are used for 360-degree images, then the encoding process is simple, but the compression performance is insufficient for massive data
Solution Approach 1:
The patent divides the 360-degree image into multiple projection formats (ERP, CMP, OHP, ISP) and processes each format separately through specific decoding steps including syntax information generation, predicted image creation, residual image combination, and format-specific rearrangement. This segmentation allows optimized compression for each projection type while maintaining overall system manageability.
Solution Approach 2:
The patent transforms the encoding approach by introducing projection format dimensions, converting a single conventional encoding process into multiple format-specific processing pathways. Each projection format has its own decoding sequence and rearrangement strategy, adding dimensional complexity that enables superior compression performance for 360-degree imagery.
2Measurement precision
If image data is processed without projection format consideration, then processing is faster, but decoding accuracy for 360-degree images deteriorates
Solution Approach 1:
The patent performs preliminary actions by generating syntax information that explicitly specifies the projection format before decoding begins. This advance preparation allows the decoder to optimize its processing path for the specific format, improving both accuracy and efficiency by avoiding generic processing steps.
Solution Approach 2:
The patent changes processing parameters based on projection format type, applying format-specific operations such as different rearrangement strategies for CMP versus ERP, or specific prediction modes for OHP. These parameter adjustments optimize decoding accuracy for each format while minimizing unnecessary processing steps.
3Productivity
If massive 360-degree image data is processed using traditional methods, then system compatibility is maintained, but processing performance is insufficient
Solution Approach 1:
The patent creates a universal decoding framework that handles multiple projection formats (ERP, CMP, OHP, ISP) through a common architecture. The system uses format identification syntax to route data through appropriate processing pathways, achieving multi-format support without requiring separate dedicated systems for each projection type.
Solution Approach 2:
The patent implements nested processing where format-specific operations are embedded within a universal decoding framework. The syntax information generation, predicted image creation, and residual combination steps form an outer layer that contains format-specific inner layers, allowing efficient organization of complex processing logic.
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.