Image Decoding With Multi-Layer OLS Indexing for HRD-DPB Mapping
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Solution Overview
Problem
The increasing demand for high-resolution and high-quality images leads to increased transmission and storage costs due to the higher amount of information required, necessitating a more efficient image compression technique.
Innovation Solution
An image decoding method and apparatus that efficiently signal HRD and DPB related syntax elements using a multi-layer OLS index, enhancing coding efficiency by accurately deriving and utilizing multi-layer Output Layer Set (OLS) indexes to prevent incorrect mapping of HRD and DPB information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional transmission and storage methods are used for high-resolution images, then image quality is maintained, but transmission cost and storage cost increase
Solution Approach 1:
The patent extracts and removes redundant information from high-resolution images through advanced compression techniques. By identifying and eliminating duplicate or unnecessary data elements, the system maintains essential image quality while significantly reducing the total data volume that requires transmission and storage, thereby lowering associated costs.
Solution Approach 2:
The patent transforms image data by changing its representation parameters through compression algorithms. By converting images from their original high-bitrate format into a more efficient compressed format with different parameter structures, the system reduces data size while preserving perceptual quality, thus decreasing transmission and storage requirements.
2Loss of energy
If image compression techniques are applied to reduce transmission and storage costs, then costs decrease, but image quality and compression efficiency may be compromised
Solution Approach 1:
The patent incorporates feedback mechanisms in the compression system where decoding performance and quality metrics are continuously monitored and fed back to optimize encoding parameters. This closed-loop approach allows the system to adjust compression settings dynamically, ensuring optimal balance between compression ratio and image quality without requiring excessive computational resources.
Solution Approach 2:
The patent employs adaptive parameter changes during compression, where key parameters such as quantization steps, transform block sizes, and prediction modes are dynamically adjusted based on local image characteristics. This allows the system to apply higher compression where tolerable and maintain better quality where needed, optimizing overall compression efficiency while preserving image quality.
3Productivity
If multi-layer OLS indexing is implemented for signaling HRD and DPB information, then coding efficiency is improved, but system complexity increases
Solution Approach 1:
The patent segments the complex signaling process into multiple manageable layers through multi-layer OLS indexing. By dividing HRD and DPB information into distinct layers with specific indexing mechanisms, the system organizes complex data structures into modular components that are easier to process and manage, thereby improving coding efficiency without overwhelming system complexity.
Solution Approach 2:
The patent introduces multi-layer OLS indexes as intermediary structures that mediate between the complex HRD/DPB information and the decoding process. These intermediary indexes act as efficient lookup tables or reference structures that simplify access to relevant parameters, reducing the computational burden on the decoder while maintaining accurate signaling of timing and buffer information.
Data Source
AI summary
An image decoding method performed by a decoding apparatus, according to the present document, comprises the steps of: deriving a multilayer output layer set (OLS) index of a target OLS in a list of multilayer OLSs; acquiring hypothetical reference decoder (HRD)-related information and decoded picture buffer (DPB)-related information for the target OLS, on the basis of the multilayer OLS index; and decoding a picture within the target OLS, on the basis of the HRD-related information and the DPB-related information, wherein the multilayer OLSs are OLSs including a plurality of layers, and the target OLS is one of the multilayer OLSs.