360-Degree Image Decoding with Projection-Aware Block Reconstruction
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, particularly for 360-degree 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 specific projection formats like Equi-Rectangular, CubeMap, OctaHedron, and IcoSahedral, with image expansion based on partitioning units and adjacent pixel information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional image encoding/decoding methods are used for 360-degree images, then the processing can be performed with standard algorithms, but the performance is insufficient for handling large data volumes and high-resolution images
Solution Approach 1:
The 360-degree image is divided into multiple projection formats (ERP, CMP, OHP, ISP) and processed separately. The decoding apparatus reconstructs the image in a specific projection format by segmenting the processing according to the projection type, which improves handling efficiency for large data volumes
Solution Approach 2:
The patent transforms the 360-degree image from spherical coordinates to various 2D projection formats (equi-rectangular, cube map, octahedron, icosahedron). This dimensional transformation enables standard 2D image processing algorithms to handle 360-degree images effectively, improving processing performance
2Manufacturing precision
If high-resolution images are processed to provide realistic media services, then the image quality improves, but the processing time and computational load increase
Solution Approach 1:
The patent performs image expansion on reference pictures before decoding to generate predicted images. This preliminary action prepares the reference data in advance, reducing the computational load during the main decoding process and decreasing overall processing time while maintaining high image quality
Solution Approach 2:
The patent uses predicted images generated from reference pictures to approximate the current image, reducing the amount of data that needs to be fully processed. This copying approach maintains high image quality while significantly reducing processing time
3Measurement precision
If image expansion is performed on reference pictures to generate predicted images, then the prediction accuracy improves, but the additional processing steps increase complexity
Solution Approach 1:
The patent performs image expansion on partitioning units of reference pictures, applying different expansion operations to different regions. This local approach improves prediction accuracy in areas where it matters most while reducing overall processing complexity compared to expanding the entire image
4Productivity
If blocks are rearranged according to region-wise packing, then the compression efficiency improves, but the rearrangement process adds computational overhead
Solution Approach 1:
The patent divides the image into partitioning units and rearranges blocks according to region-wise packing within each unit. This segmentation approach improves compression efficiency by organizing data in memory-friendly patterns while keeping the rearrangement complexity manageable through localized processing
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.