CCSAO Filtering Across Virtual Boundaries for Video Decoding
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Solution Overview
Problem
Video coding techniques face challenges in performing filtering operations across virtual boundaries, which can prevent effective sample enhancement due to the unavailability of neighboring samples on one side of the boundary, leading to inefficiencies in bitrate and fidelity of decoded video data.
Innovation Solution
Implementing cross-component sample adaptive offset (CCSAO) and bilateral interpolation filtering (BIF) techniques that allow filtering across virtual boundaries by using neighboring samples on the same side of the boundary and increasing the minimum padding size for adaptive loop filtering (ALF) and cross-component adaptive loop filtering (CCALF) to ensure effective sample enhancement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If virtual boundaries are used to allow parallel encoding and decoding of video pictures, then processing efficiency is improved, but filtering operations cannot be performed across boundaries leading to reduced video quality
Solution Approach 1:
The video picture is divided into multiple tiles with virtual boundaries, allowing parallel processing of each tile while maintaining the option to perform filtering operations across these boundaries when beneficial, thus balancing segmentation benefits with quality requirements
Solution Approach 2:
The patent applies filtering operations partially across virtual boundaries - specifically, filtering is performed when it improves video quality, and the degree of filtering is adjusted based on the boundary context, allowing selective application rather than complete prohibition or universal application
2Manufacturing precision
If filtering operations are performed across virtual boundaries using neighboring samples, then video quality is improved, but additional computational complexity is required
Solution Approach 1:
The patent applies different filtering strategies to different regions - samples near virtual boundaries receive specialized handling with selective filtering, while interior samples use standard filtering, thus optimizing quality where needed without uniformly increasing complexity across the entire picture
Solution Approach 2:
The patent prepares reference samples and determines filtering parameters in advance before performing the actual filtering operation, reducing the computational burden during the critical filtering stage by pre-processing available data
3Manufacturing precision
If minimum padding size is increased for adaptive loop filtering, then sample enhancement effectiveness is improved, but memory requirements and processing overhead increase
Solution Approach 1:
The patent dynamically adjusts the padding size based on the specific filtering context and boundary conditions rather than using a fixed large padding for all cases, allowing the system to use only the necessary amount of padding memory for each filtering operation
Data Source
AI summary
An example device for decoding video data includes a memory configured to store video data; and one or more processors implemented in circuitry and configured to: decode a current block of the video data to form a decoded block; determine that a current sample of the decoded block neighbors a sample along a virtual boundary in the decoded block and neighbors one or more samples that are not along any virtual boundary in the decoded block; compute band information for cross component sample adaptive offset (CCSAO) for the current sample using at least one of the one or more samples that are not along any virtual boundary in the decoded block and without using the sample along the virtual boundary; and perform CCSAO on the current sample using the band information.