360-Degree Image Decoding Across Projection Formats for Better Compression
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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 high-resolution and high-quality 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 image in various projection formats, such as Equi-Rectangular, CubeMap, OctaHedron, and IcoSahedral, to enhance compression performance.
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 high-resolution images
Solution Approach 1:
The patent divides the 360-degree image into multiple projection formats (ERP, CMP, OHP, ISP) and processes different regions with different encoding strategies. The image is segmented into face regions, edge regions, and corner regions, each handled with appropriate prediction and transformation methods to optimize compression while managing complexity.
Solution Approach 2:
The patent transforms the 360-degree image from spherical coordinates to multiple 2D projection formats. By converting the 3D spherical image into 2D representations (equirectangular, cubemap, octahedron, icosahedron), the encoding process can apply conventional 2D video coding techniques while maintaining the ability to represent the full 360-degree view, thus improving compression performance without excessive complexity increase.
2Measurement precision
If high-resolution 360-degree images are processed, then image quality is improved, but the data volume increases massively
Solution Approach 1:
The patent applies different encoding precision and compression strategies to different regions of the 360-degree image. Face regions (central viewing areas) are encoded with higher quality and less compression, while edge and corner regions use higher compression ratios. This regional differentiation maintains perceptual quality for important areas while reducing overall data volume.
Solution Approach 2:
The patent combines multiple projection formats (ERP, CMP, OHP, ISP) into a unified encoding framework. By merging these different representation methods, the system can select the most efficient format for different image regions and combine them into a single compressed bitstream, reducing total data volume while preserving high-resolution quality where needed.
3Adaptability or versatility
If multiple projection formats are supported, then versatility is improved, but device complexity increases
Solution Approach 1:
The patent creates a universal encoding framework that can handle multiple projection formats (ERP, CMP, OHP, ISP) through a single unified process. The encoder is designed to accept input in any of these formats and apply appropriate transformation and compression methods, making the system multi-functional without requiring separate encoding pipelines for each format.
Solution Approach 2:
The patent manages complexity by changing parameters rather than structure. Instead of implementing fundamentally different encoding algorithms for each projection format, the system adjusts parameters such as block size, transformation type, and prediction modes based on the input format. This parameter-based approach allows versatile format support while keeping the core processing logic relatively simple and reusable.
Data Source
AI summary
Disclosed are methods and apparatuses for decoding an image. A method includes receiving a bitstream obtained by encoding the image; dividing a first coding block into a plurality of second coding blocks; generating a prediction block of a second coding block based on syntax information obtained from the bitstream; and reconstructing the second coding block based on the prediction block and a residual block of the second coding block, the residual block being obtained by performing a dequantization and an inverse-transform on quantized transform coefficients from the bitstream. The first coding block has a recursive division structure. The first coding block is divided based on at least one of a quad tree division, a binary tree division or a triple tree division.