360 Image Decoding with Reference Picture Scaling for 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, necessitating 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 specific projection formats, such as Equi-Rectangular, CubeMap, OctaHedron, and IcoSahedral, while utilizing motion vector candidates and reference pictures for enhanced compression.
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 360-degree image is divided into multiple projection formats (ERP, CMP, OHP, ISP) and processed separately. The encoding apparatus selectively applies different encoding strategies based on the projection format, allowing optimized compression for each type while maintaining overall system manageability
Solution Approach 2:
The patent performs image expansion on reference pictures before motion compensation. By pre-expanding the reference picture to match the current picture's resolution, the system avoids complex real-time resizing operations during encoding while improving motion estimation accuracy and compression performance
2Manufacturing precision
If image resolution is increased for high-quality 360-degree images, then image quality improves, but the data amount increases massively
Solution Approach 1:
The patent changes the resolution parameter of reference pictures dynamically. Reference pictures are expanded to match the current picture's resolution only when needed for motion compensation, otherwise lower resolution is used. This parameter adaptation maintains high image quality where necessary while reducing overall data volume
Solution Approach 2:
Instead of storing and processing multiple high-resolution 360-degree images simultaneously, the system uses motion compensation to generate predicted images from lower-resolution reference pictures. This copying approach reduces data storage requirements while maintaining visual quality through predictive reconstruction
3Loss of energy
If motion compensation is performed without image expansion, then processing speed is faster, but compression performance deteriorates
Solution Approach 1:
The reference picture is expanded to match the current picture's resolution before motion compensation is performed. This preliminary expansion ensures that motion estimation operates on matching resolutions, improving compression performance without requiring complex real-time adaptive resizing during the compensation phase
Solution Approach 2:
Image expansion is applied selectively only to reference pictures that will be used for motion compensation, not to all images in the system. This localized application of expansion maintains processing efficiency while improving compression performance where it matters most
Data Source
AI summary
A method of decoding an image, includes obtaining at least one offset for a picture, deriving a variable for scaling for the picture based on the at least one offset, and performing inter prediction based on the variable for scaling for the picture. The at least one offset is defined with a direction of scaling.