Merge Candidate Reconfiguration for Efficient Image Encoding
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Solution Overview
Problem
High-resolution and high-quality image data requires high-efficiency encoding/decoding techniques to reduce the increased costs of transmission and storage due to its large data volume.
Innovation Solution
An image encoding/decoding method that utilizes a reconfigured merge candidate list, calculated using distortion values from neighbor blocks, to improve compression efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If high-resolution and high-quality image data is used, then image quality is improved, but data volume increases leading to higher transmission and storage costs
Solution Approach 1:
The patent changes the parameters of the merge candidate list by reconfiguring it based on distortion values calculated from neighbor blocks. This involves dynamically adjusting the list composition and ordering based on local image characteristics, thereby achieving efficient compression that maintains high image quality while reducing data volume for transmission and storage.
Solution Approach 2:
The patent applies local quality by calculating distortion values specifically for neighbor blocks and using these local distortion metrics to reconfigure the merge candidate list. This localized approach allows the encoding process to adapt to specific regional characteristics of the image, optimizing compression efficiency without compromising overall image quality.
2Loss of energy
If conventional image compression techniques are used, then transmission and storage costs are reduced, but compression efficiency is insufficient for high-resolution images
Solution Approach 1:
The patent implements feedback by calculating distortion values from neighbor blocks and using these distortion metrics to reconfigure the merge candidate list. This feedback loop continuously optimizes the compression process based on actual image characteristics, achieving superior compression efficiency compared to conventional techniques while maintaining cost-effectiveness for high-resolution image transmission and storage.
Solution Approach 2:
The patent introduces dynamics by making the merge candidate list reconfigurable rather than fixed. The list is dynamically adjusted based on distortion values calculated from neighbor blocks, allowing the compression algorithm to adapt its behavior in real-time according to the specific characteristics of the image data being processed, thereby significantly improving compression efficiency.
3Productivity
If inter-prediction is performed without reconfiguring merge candidate list, then processing speed is maintained, but compression efficiency is suboptimal
Solution Approach 1:
The patent applies preliminary action by pre-calculating distortion values for neighbor blocks before reconfiguring the merge candidate list. This preliminary computation enables the system to prepare optimization parameters in advance, allowing the main compression process to benefit from the reconfigured list without significant increases in overall processing time or complexity.
Data Source
AI summary
An image encoding/decoding method may comprise deriving at least one merge candidate of a current block, generating an initial merge candidate list of the current block by using the derived at least one merge candidate and generating a reconfigured merge candidate list by using the initial merge candidate list.