Video Encoding Mode Selection by Temporal Layer Depth
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Solution Overview
Problem
Current video encoding methods face challenges in efficiently determining encoding modes for pictures based on their temporal layer depths, leading to suboptimal compression rates and image quality due to limitations in predicting pixel values and managing residual data.
Innovation Solution
A method and device for determining encoding modes applicable to a current picture according to its temporal layer depth, generating prediction and transform blocks, and reconstructing the picture using these blocks to improve encoding efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the number of prediction methods is increased to improve prediction accuracy, then the prediction precision is improved, but the amount of encoding information required to specify the prediction methods increases
Solution Approach 1:
The prediction methods are segmented into two categories: intra-prediction methods for spatial redundancy removal and inter-prediction methods for temporal redundancy removal. This segmentation allows the encoder to select appropriate prediction types based on the characteristics of the current block, improving prediction accuracy while managing encoding information efficiently through context-based selection
Solution Approach 2:
The encoder dynamically selects between intra-prediction and inter-prediction methods based on block characteristics and context, rather than using a fixed prediction approach. This dynamic selection allows the system to adapt to different picture regions and temporal layers, optimizing prediction accuracy while controlling the amount of encoding information required
2Measurement precision
If data units are split into smaller blocks to improve prediction accuracy, then the prediction precision is improved, but the complexity of encoding increases due to more blocks requiring processing
Solution Approach 1:
The picture is segmented into multiple data units that can be further divided into blocks of various sizes. This hierarchical segmentation allows the encoder to process smaller blocks where needed for high accuracy while using larger blocks in other regions, balancing prediction accuracy with encoding complexity through adaptive block size selection
Solution Approach 2:
Different block sizes and prediction methods are applied to different regions of the picture based on local characteristics. Complex prediction methods and smaller blocks are used in regions requiring high precision, while simpler methods and larger blocks are used in other regions, optimizing the balance between accuracy and complexity
3Productivity
If lossy compression is applied to residual data to reduce data size, then the compression rate is improved, but the image quality deteriorates due to data loss
Solution Approach 1:
The system creates multiple copies of the video stream with different temporal layer depths, where each copy contains residual data at different compression levels. This allows the decoder to select the appropriate quality level based on available bandwidth and storage, achieving high compression rates for lower quality versions while preserving options for higher quality reconstruction when needed
Data Source
AI summary
Provided is a video decoding method including determining encoding modes applicable to a current picture according to a temporal layer depth of the current picture, generating a prediction block and a transform block for blocks included in the current picture according to the encoding modes applicable to the current picture, and reconstructing the current picture by using the prediction block and the transform block.