Hierarchical Epitome Construction for Scalable Video Encoding
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Solution Overview
Problem
Existing scalable video coding techniques face challenges in maintaining consistency and predictability of information between different resolution levels, leading to suboptimal prediction quality and inefficient coding, particularly in SVC encoders/decoders.
Innovation Solution
A hierarchical epitome construction method is introduced, where an epitome is generated at the maximum resolution level and at least one at a lower resolution level, ensuring consistency and predictability across layers, and used for intra-layer or inter-layer prediction, with refinement steps to improve prediction quality and coding efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional scalable video coding is used with inter-layer prediction, then multiple clients with different characteristics can be served, but consistency and predictability of information between different resolution levels deteriorates
Solution Approach 1:
The patent segments the video coding process into distinct resolution levels (base layer and enhancement layers), with each layer having its own epitome construction and prediction process. This segmentation allows independent optimization at each level while maintaining overall consistency through the hierarchical structure, resolving the contradiction between serving multiple clients and maintaining information consistency.
Solution Approach 2:
The patent performs preliminary action by constructing epitomes at each resolution level before the actual prediction process. These pre-constructed epitomes serve as reliable reference structures that ensure consistency across layers, allowing the system to maintain predictability while adapting to different client requirements through the hierarchical arrangement.
2Measurement precision
If resolution levels are increased in hierarchical epitome, then prediction quality improves, but data transmission volume increases
Solution Approach 1:
The patent implements a nested structure where lower-resolution epitomes are embedded within higher-resolution epitomes in a hierarchical arrangement. This nesting allows prediction quality to improve at higher resolutions while maintaining data efficiency, as the lower-resolution information is reused and refined rather than completely replaced, thus resolving the contradiction between prediction quality and data volume.
Solution Approach 2:
The patent applies local quality by allowing different resolution levels to have different levels of detail and complexity appropriate to their specific requirements. Base layers use coarser epitomes for efficient transmission, while enhancement layers use finer epitomes for improved prediction quality, optimizing the balance between data volume and prediction accuracy at each local level.
Data Source
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AI summary
The invention relates to a method for encoding an image sequence, outputting a signal having a layered organisation comprising at least a base layer and an enhancement layer. According to the invention, for at least one current image of the sequence, said method performs a step consisting of the construction of a hierarchical epitome associated with the enhancement layer corresponding to the maximum resolution level of the current image, using: an epitome associated with the enhancement layer corresponding to the maximum resolution level of the current image; and at least one epitome associated with a preceding layer corresponding to a resolution level lower than the maximum resolution level of the current image.