360-Degree Image Decoding With Projection-Aware MPM Reconfiguration
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Solution Overview
Problem
The existing image processing systems face challenges in efficiently handling the massive data generated by 360-degree images for virtual and augmented reality, requiring improved performance in image encoding and decoding methods.
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 specific 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
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 large data volumes
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:
Different encoding parameters and prediction modes are applied to different regions of the 360-degree image. Face regions use one set of encoding parameters while edge and corner regions use different parameters optimized for their specific characteristics, improving overall compression performance without uniformly increasing complexity across the entire image.
2Reliability
If 360-degree images are captured with multiple cameras for VR/AR, then the quality and realism are improved, but the data volume increases massively
Solution Approach 1:
The patent merges multiple camera inputs into unified projection formats (ERP, CMP, OHP, ISP) that represent the entire 360-degree environment. By combining the data from multiple cameras into these standardized projections and applying efficient encoding, the system maintains the quality benefits of multi-camera capture while reducing the overall data volume through coordinated processing.
Solution Approach 2:
The patent transforms the image data between different projection formats and applies various encoding parameters (quantization, transformation, prediction modes) to compress the data. By changing the representation parameters of the image data and using format-specific optimization, the system reduces data volume while preserving the quality achieved through multi-camera capture.
3Measurement precision
If image expansion is performed on reference pictures for prediction, then the prediction accuracy is improved, but the processing time increases
Solution Approach 1:
The patent performs image expansion on reference pictures in advance before the actual encoding/decoding process. By pre-processing the reference pictures and expanding them to the required resolution, the system improves prediction accuracy during decoding without adding significant processing time during the critical encoding/decoding operation itself.
Solution Approach 2:
The patent applies image expansion selectively to reference pictures only when needed for prediction, rather than expanding all images uniformly. This partial application of expansion reduces unnecessary processing time while still achieving improved prediction accuracy where it provides the most benefit.
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.