Tile Group Partitioning for Region-of-Interest Video Decoding
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Solution Overview
Problem
Existing video data compression techniques struggle with efficient referencing and processing of video stream portions, particularly in applications like omnidirectional video, where traditional slice structures are inflexible and do not support region-of-interest decoding or sub-picture extraction.
Innovation Solution
The implementation of a rectangular picture area definition and motion-constrained tile sets (MCTS) within video data streams, allowing for independent decoding and rendering of sub-pictures, and enabling region-of-interest decoding and viewport-dependent video processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional slice structures are used in video data compression, then the video stream can be decoded using conventional methods, but the system cannot support region-of-interest decoding or sub-picture extraction in omnidirectional video
Solution Approach 1:
The video picture is divided into multiple independent tiles, where each tile can be decoded and processed separately. This segmentation enables region-of-interest decoding by allowing selective decoding of specific tiles rather than requiring decoding of the entire picture, thus improving adaptability while maintaining manageable complexity through standardized tile structures
Solution Approach 2:
The patent introduces a tile-based dimensional structure that overlays the traditional slice structure, creating a two-dimensional tile grid system. This additional dimensional organization enables flexible region-of-interest extraction by allowing arbitrary rectangular regions to be formed through tile combinations, enhancing versatility without fundamentally changing the underlying decoding complexity
2Adaptability or versatility
If motion-constrained tile sets (MCTS) are implemented for independent decoding, then sub-picture extraction and viewport-dependent processing are enabled, but the decoding structure becomes more complex
Solution Approach 1:
The picture is divided into multiple tiles that can be independently decoded, with each tile containing complete decoding information including motion constraints. This segmentation enables sub-picture extraction by allowing selective rendering of specific tile sets while maintaining independent decoding capability through self-contained tile structures
Solution Approach 2:
Motion constraints are pre-defined and embedded in the tile structure during encoding, allowing decoders to perform viewport-dependent processing without complex runtime calculations. The preliminary establishment of motion-constrained tile sets enables efficient sub-picture extraction by pre-organizing tiles into independently decodable units with predetermined motion boundaries
3Productivity
If flexible partitioning is used for tile groups, then region-of-interest decoding efficiency improves, but processing overhead increases
Solution Approach 1:
Tiles are organized into tile groups with flexible partitioning that adapts to region-of-interest requirements. Each tile group contains tiles that can be selectively processed, improving decoding efficiency for specific regions while maintaining standardized group structures that limit processing overhead through regular partitioning patterns
Solution Approach 2:
The tile group partitioning structure is designed to be dynamically adaptable to different region-of-interest configurations while maintaining a standardized base format. This dynamic flexibility allows efficient processing of arbitrary rectangular regions through simple tile group combinations without requiring complex custom processing for each region, thus improving productivity while controlling overhead
Data Source
AI summary
A system may identify a defined rectangular picture area and render video corresponding to the defined rectangular picture area. The system may receive a video bitstream comprising a picture having a header and may receive data specifying a structure of the picture. The system may parse the data specifying the structure of the picture for an identifier corresponding to a defined rectangular area in the first picture and for a tile index of a top left tile in the defined rectangular area. The system may determine one or more tiles comprised in the defined rectangular area based on the identifier corresponding to the defined rectangular area and the tile index of the top left tile. The system may reconstruct the picture including a sub-picture that comprises the defined rectangular area based upon the identifier corresponding to the defined rectangular area. The computing system may render the sub-picture in the defined rectangular area.