Projection-Format 360-Degree Image Decoding for Better Compression
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, requiring improved performance in image encoding and decoding, particularly for 360-degree images.
Innovation Solution
A method for decoding 360-degree images involves generating a predicted image using syntax information, combining it with a residual image, and reconstructing the image in specific projection formats like Equi-Rectangular, CubeMap, OctaHedron, and IcoSahedral, with image expansion based on partitioning units to enhance compression performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If multi-view images are processed for 360-degree images, then the amount of data generated increases massively, but the performance of image processing system is insufficient
Solution Approach 1:
The patent divides the 360-degree image into multiple equirectangular images (first, second, third, and fourth equirectangular images) representing different regions of the spherical projection. Each region is processed and encoded independently, which reduces the computational complexity for each segment while maintaining the complete 360-degree coverage. This segmentation strategy directly addresses the contradiction by making the processing of large 360-degree data more manageable and efficient.
Solution Approach 2:
The patent transforms the 360-degree equirectangular image into a cubemap projection by generating multiple image sets corresponding to different faces of a cube. This dimensional transformation allows the system to represent spherical data in a way that improves compression efficiency and processing performance. The cubemap representation enables better utilization of spatial frequencies and reduces redundant information, thereby improving productivity while handling large data quantities.
2Loss of energy
If 360-degree images are encoded using conventional methods, then compression performance is insufficient, but processing time and computational complexity increase
Solution Approach 1:
The patent segments the 360-degree image into multiple equirectangular images representing different regions, and further divides each region into multiple blocks. This segmentation allows for targeted compression techniques to be applied to each block independently, improving compression efficiency without requiring the entire large image to be processed at once, thereby reducing processing time.
Solution Approach 2:
The patent applies different compression parameters and techniques to different blocks and regions of the 360-degree image based on their local characteristics. Important regions can be processed with higher quality settings while less important regions use lower quality settings, optimizing the balance between compression performance and processing time. This local quality approach enables efficient resource utilization and faster processing.
Data Source
AI summary
Disclosed are methods and apparatuses for image data encoding/decoding. A method for decoding a 360-degree image includes the steps of: receiving a bitstream obtained by encoding a 360-degree image; generating a prediction image by making reference to syntax information obtained from the received bitstream; adding the generated prediction image to a residual image obtained by dequantizing and inverse-transforming the bitstream, so as to obtain a decoded image; and reconstructing the decoded image into a 360-degree image according to a projection format. Therefore, the performance of image data compression can be improved.