Video Quantization Parameter Prediction Using Temporal Frame History
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Solution Overview
Problem
Existing video encoding/decoding technologies face challenges in efficiently generating quantization parameters, leading to increased data requirements due to the growing image size, resolution, and frame rate, necessitating improved encoding efficiency and image enhancement.
Innovation Solution
A method and apparatus for generating predicted quantization parameters using quantization parameters from previously encoded/decoded frames, involving decoding prediction information and delta quantization parameters, and calculating a predicted quantization parameter through addition with a delta quantization parameter.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If video data is stored or transmitted without compression processing, then image quality is preserved, but data volume and hardware resource requirements increase significantly
Solution Approach 1:
The patent extracts and removes redundant information from video data through compression algorithms. The encoder identifies and eliminates temporal redundancy (between frames), spatial redundancy (within frames), and psychovisual redundancy (information less perceptible to human vision), keeping only the essential data needed to reconstruct the video with acceptable quality.
Solution Approach 2:
The patent applies compression processing in advance during the encoding stage before storage or transmission. The video data is pre-processed to reduce its size, and the compressed data is stored or transmitted. The decoder then reconstructs the video data when needed, avoiding the need to handle large uncompressed data volumes during storage or transmission.
2Manufacturing precision
If image size, resolution, and frame rate are increased, then video quality is improved, but the amount of data to be encoded increases
Solution Approach 1:
The patent dynamically adjusts compression parameters such as quantization parameter (QP), transform block size, and prediction mode based on the complexity of the video content and the desired quality level. When high quality is needed (higher resolution/frame rate), the encoder uses finer quantization and more sophisticated prediction modes, while still applying compression to manage the increased data volume.
Solution Approach 2:
The patent employs adaptive compression techniques where the compression ratio and processing intensity vary dynamically based on scene complexity, motion levels, and content characteristics. This allows the system to maintain high video quality for complex scenes while applying more aggressive compression for simpler scenes, optimizing the balance between quality and data volume.
3Productivity
If quantization parameters are generated without using predicted quantization parameters from previous frames, then encoding simplicity is maintained, but encoding efficiency decreases
Solution Approach 1:
The patent implements a feedback mechanism where the quantization parameters from previously encoded/decoded frames are used to predict the quantization parameters for the current frame. The encoder calculates the predicted QP based on historical data and adjusts the current QP accordingly, improving encoding efficiency by leveraging temporal correlations in video content while maintaining manageable encoding complexity through systematic prediction algorithms.
Data Source
AI summary
Disclosed are a method and an apparatus for generating quantization parameters for generating a predicted quantization parameter for a quantization group using quantization parameters of a frame encoded/decoded previously in time. The method and the apparatus may achieve an effect of improving prediction performance for the quantization parameter.