Large Model Frame Type Determination for Video Encoding
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Traditional video encoding and transcoding methods require complex computation to determine frame types, often failing to find the optimal combination, which affects compression efficiency.
Innovation Solution
A method and apparatus that use a large model to determine frame types by obtaining video frame sequences, performing image feature extraction, calculating feature similarity between frames, and training a model to predict frame types based on this similarity, thereby improving recognition accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional complex computation methods are used to determine frame types, then comprehensive analysis can be performed, but computational complexity increases and optimal frame type combinations are difficult to find
Solution Approach 1:
The patent replaces traditional mechanical computation methods with a large model (deep learning-based neural network) to determine frame types. The large model processes video frames and adjacent frame information to predict optimal frame types (I-frame, P-frame, or B-frame), substituting complex iterative computational algorithms with a trained neural network that provides both accuracy and efficiency.
Solution Approach 2:
The patent introduces a large model as an intermediary between raw video data and frame type determination. This intermediary processes extracted features and adjacent frame information to produce accurate frame type predictions, acting as a mediator that simplifies the overall system while improving performance.
2Productivity
If traditional encoding methods are used, then standard compatibility is maintained, but compression efficiency is limited
Solution Approach 1:
The patent changes the approach to frame type determination by using a trained large model that outputs probability distributions for different frame types. This parameter-based approach (using prediction probabilities) enables more efficient compression decisions compared to traditional fixed algorithms, improving compression efficiency while managing encoding complexity.
3Productivity
If frame type determination is simplified, then processing speed increases, but recognition accuracy decreases
Solution Approach 1:
The patent applies preliminary action by training the large model in advance on extensive video data. This pre-training phase captures complex patterns and relationships, allowing the model to make accurate frame type predictions during actual encoding without requiring complex real-time computations. The heavy lifting is done beforehand, enabling fast and accurate inference.
Solution Approach 2:
The patent uses a large model that has learned and copied patterns from training data to make predictions on new video frames. Instead of performing complex analysis on each frame, the system copies learned patterns from the training phase to rapidly determine frame types, maintaining high accuracy while improving processing speed.
Data Source
AI summary
A method for determining a frame type based on a large model, includes: obtaining a video frame sequence from video data; obtaining a feature of each video frame by performing image feature extraction on each of video frames in the video frame sequence; obtaining a feature similarity by comparing each video frame with an adjacent video frame in the video frame sequence; and determining a frame type of each video frame in the video frame sequence based on the feature similarity between each video frame and the adjacent video frame.