Projection-Aware 360-Degree Image Decoding for Compression Efficiency
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Solution Overview
Problem
The existing image processing systems struggle with the massive data generated from 360-degree images for 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 decoding 360-degree images involves receiving a bitstream, generating a predicted image using syntax information, combining it with a residual image, and reconstructing the image in a projection format, utilizing techniques like Equi-Rectangular Projection, CubeMap Projection, and OctaHedron Projection 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 each format separately with format-specific optimization. The image is segmented into multiple regions or blocks that can be encoded independently, allowing parallel processing and reduced complexity while maintaining high compression performance for each segment.
Solution Approach 2:
The patent applies different encoding strategies to different regions of the 360-degree image based on their characteristics. High-priority regions (such as front-facing areas in ERP or specific faces in CMP) receive higher quality encoding with more bits, while less important regions use lower quality encoding, optimizing overall compression performance while managing complexity.
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 combines multiple projection formats and encoding techniques into a unified framework. By merging the advantages of different projection formats (ERP's simplicity, CMP's distortion reduction, OHP's compactness, ISP's efficiency) and applying them together with region-wise packing and adaptive quantization, the system achieves high resolution with reduced data volume compared to processing a single format at full resolution.
Solution Approach 2:
The patent dynamically adjusts encoding parameters such as quantization step size, transformation block size, and prediction mode based on the region importance and projection format. This parameter adaptation allows high-resolution encoding in critical areas while using lower parameters in less important areas, significantly reducing overall data volume while maintaining perceived image quality.
3Productivity
If region-wise packing is applied to rearrange blocks, then compression efficiency is improved, but the processing time increases
Solution Approach 1:
The patent performs region-wise packing and block rearrangement as preliminary operations before the main encoding process. By pre-organizing the image data into optimally packed regions and determining the encoding strategy in advance, the actual encoding process becomes faster and more efficient, reducing overall processing time while maintaining high compression efficiency.
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.