Video Frame Feature Restoration for High-Compression Image Quality
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Solution Overview
Problem
Existing video compression techniques, such as Advanced Video Coding and High-Efficiency Video Coding, struggle to maintain high image quality while achieving efficient data compression, particularly in frames like B and P frames, due to the loss of intra-frame information during encoding.
Innovation Solution
Implementing a feature restoration model, such as a neural network, to enhance the image quality of low-quality frames by utilizing high-quality reference frames, through processes like dequantization and motion compensation, to generate higher-quality frames based on restored reference and residual data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If video compression is applied to reduce data transmission volume, then data transmission efficiency is improved, but image quality deteriorates
Solution Approach 1:
The patent segments the image restoration process into multiple stages: extracting features from compressed frames, selecting reference frames, and progressively restoring details. This segmentation allows the system to handle compression artifacts systematically while maintaining quality improvements without requiring transmission of full-resolution data.
Solution Approach 2:
The patent performs preliminary actions by pre-selecting reference frames and pre-extracting features from compressed video frames before the actual restoration process. This preliminary preparation enables more efficient quality enhancement during real-time processing, as the computational groundwork is already in place.
2Quantity of substance
If advanced video coding techniques are used to compress video, then compression rate is improved, but temporal redundancy removal efficiency deteriorates
Solution Approach 1:
The patent implements feedback mechanisms where restored high-quality frames are used as reference frames for subsequent compression and restoration operations. This feedback loop allows the system to continuously improve restoration quality while maintaining efficient compression, as the reference frames adapt to the actual content being processed.
Solution Approach 2:
The patent introduces feature extraction and reference frame selection as intermediary processes between compression and final restoration. These intermediaries bridge the gap between compressed data and quality restoration, enabling efficient handling of temporal redundancy without sacrificing compression effectiveness.
3Manufacturing precision
If feature restoration models are implemented to enhance image quality, then image quality is improved, but computational complexity increases
Solution Approach 1:
The patent applies partial action by selectively restoring features only in regions where compression artifacts are most prominent, rather than uniformly processing the entire frame. This approach maintains quality improvements while significantly reducing computational complexity compared to full-frame restoration.
Solution Approach 2:
The patent uses copying by replicating features from reference frames to corresponding regions in current frames during the restoration process. This copying mechanism avoids redundant computational operations while maintaining restoration quality, as similar regions can be efficiently replicated rather than reprocessed.
Data Source
AI summary
An electronic device, including one or more processors configured to execute instructions; and a memory storing the instructions which, when executed by the one or more processors, configures the one or more processors to generate high-quality feature data of a current frame, by implementing a feature restoration model that is provided reference feature data, received by an electronic device, and corresponding to compressed feature data of a reference image corresponding to a first time that is different from a second time to which the current frame corresponds, and low-quality feature data received by the electronic device, and corresponding to compressed data of the current frame, that has a lower image quality than the reference image; and generate a current frame of a third image quality higher than the second image quality, based on the high-quality feature data.