Synchronized Sparse Coding Dictionaries for Video Compression
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional dictionaries trained from generic images or video frames are less optimal for representing video content, leading to inefficient compression and increased costs in video transmission and storage due to higher bit rates required for maintaining quality.
Innovation Solution
A system and method for generating and synchronizing sparse coding dictionaries based on original video frames from a video session, allowing for efficient encoding and decoding of video content by utilizing unique identifiers to facilitate dictionary synchronization across communication sessions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If conventional dictionaries trained from generic images or video frames are used, then device complexity is reduced, but manufacturing precision deteriorates because the dictionaries are less optimal for representing specific video content
Solution Approach 1:
The system generates and trains dictionaries in advance during initialization or setup phases, so that when actual video encoding is needed, pre-trained dictionaries are already available. This preliminary dictionary generation based on representative video content allows high accuracy without adding complexity to the real-time encoding process.
Solution Approach 2:
Instead of creating entirely new dictionaries for each video content, the system copies and adapts dictionary structures from previously trained dictionaries. By copying the framework and selectively updating elements based on specific video content characteristics, the system achieves content-specific accuracy while reusing existing computational resources.
2Manufacturing precision
If more bits are used for transmission, then manufacturing precision improves by maintaining higher media quality, but loss of substance increases due to higher transmission costs and bandwidth consumption
Solution Approach 1:
The system changes the parameter of dictionary representation by using sparse coding with learned dictionaries that capture essential video content characteristics. This allows achieving high reconstruction quality with fewer bits by transforming the encoding parameters from conventional transform-based methods to dictionary-based sparse representations.
Solution Approach 2:
The patent replaces conventional mechanical compression mechanisms (fixed transform codes) with a learned dictionary-based system that adapts to video content. This substitution enables more efficient bit utilization by using data-driven dictionaries that model video structures more effectively than traditional fixed transforms.
3Productivity
If conventional compression schemes are used, then ease of operation is maintained, but productivity deteriorates because higher bit rates are required to achieve acceptable quality
Solution Approach 1:
The system performs self-service by automatically training and adapting dictionaries based on the specific video content being encoded. The dictionary management component learns from the video data itself, eliminating the need for manual dictionary creation or extensive configuration, thereby maintaining ease of operation while achieving superior compression efficiency.
Solution Approach 2:
The system incorporates feedback mechanisms where encoding results and reconstruction errors are used to iteratively improve dictionary quality. This feedback loop allows the system to automatically optimize its compression performance for different video content types without external intervention, balancing productivity improvement with operational simplicity.
Data Source
AI summary
Techniques for generating synchronized dictionaries for sparse coding to facilitate coding of video content are presented. During a communication session, an encoder associated with a first terminal generates a dictionary that is synchronized with a corresponding dictionary maintained by a decoder associated with a second terminal. The decoder uses the dictionary to facilitate decoding video content, based on sparse coding, received from the encoder. During a subsequent communication session between the first terminal and a third terminal, the dictionary is made available to a decoder associated with the third terminal. The encoder associated with the first terminal and decoder associated with the third terminal signal each other to identify the dictionary as being common between the encoder and decoder, and this common dictionary is used for coding content communicated between the encoder and decoder. The encoder and decoder can update the dictionary based on the subsequent video session.