360-Degree Image Decoding with Adaptive MPM Reconfiguration
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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, necessitating 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 a projection format, utilizing techniques like Equi-Rectangular Projection, CubeMap Projection, and IcoSahedral Projection, with image expansion and intra-prediction to enhance compression performance.
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 processing performance is insufficient and cannot handle the massive data volume efficiently
Solution Approach 1:
The patent divides the 360-degree image into multiple projection formats (ERP, CMP, OHP, ISP) and processes different regions using appropriate prediction methods. The image is segmented into prediction units where intra-prediction is applied to exploit spatial redundancy within each projection format, thereby improving processing efficiency for the massive data volume.
Solution Approach 2:
The patent changes the parameter of projection format to optimize processing. By selecting different projection formats (ERP for equirectangular, CMP for cubemap, OHP for octahedron, ISP for icosahedron) based on the specific 360-degree image characteristics, the encoding/decoding performance is improved to handle large data volumes efficiently.
2Quantity of substance
If image compression is applied to reduce data volume, then the amount of data is reduced, but the compression performance needs enhancement to achieve better efficiency
Solution Approach 1:
The patent employs intra-prediction where the decoded image is used as a reference for predicting subsequent blocks. This feedback mechanism exploits spatial redundancy within the image, achieving better compression performance by predicting pixel values from already decoded neighboring blocks, thereby reducing the actual data that needs to be stored and transmitted.
Solution Approach 2:
The patent performs projection format conversion and image expansion in advance before the main encoding process. By preliminarily transforming the 360-degree image into standard projection formats and expanding the image data structure, the subsequent compression process becomes more efficient, achieving better overall compression performance.
3Adaptability or versatility
If projection format conversion is performed to reconstruct the 360-degree image, then the image can be displayed in various formats, but the processing complexity increases
Solution Approach 1:
The patent implements a universal decoding framework that can handle multiple projection formats (ERP, CMP, OHP, ISP) through a single apparatus. The decoding device is designed with multi-functionality to perform projection format conversion and image reconstruction for different 360-degree image types, reducing the need for separate processing pipelines for each format.
Solution Approach 2:
The patent introduces an intermediate representation layer where the 360-degree image is first decoded into a standardized format, and then converted to the target projection format. This intermediary step simplifies the overall processing complexity by breaking down the complex format conversion into manageable stages with clear interfaces.
4Measurement precision
If image expansion is performed on reference pictures to generate predicted images, then the prediction accuracy is improved, but the processing time and computational load increase
Solution Approach 1:
The patent applies image expansion selectively rather than to the entire reference picture. By performing expansion only on specific regions or using simplified expansion methods where full expansion is not necessary, the prediction accuracy is improved in critical areas while the overall processing time and computational load are kept manageable.
Data Source
AI summary
A method for decoding a 360-degree image includes: 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; combining the generated prediction image with 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. Here, generating the prediction image includes: checking, from the syntax information, prediction mode accuracy for a current block to be decoded; determining whether the checked prediction mode accuracy corresponds to most probable mode (MPM) information obtained from the syntax information; and when the checked prediction mode accuracy does not correspond to the MPM information, reconfiguring the MPM information according to the prediction mode accuracy for the current block.