Video Proxy Codec Pre-Processor for Compression Artifact Compensation
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Solution Overview
Problem
Existing video codecs introduce visually undesirable artifacts during compression, and new standards take years to develop, necessitating a solution to improve compression quality without changing the codec's standard.
Innovation Solution
A pre-processor is trained using a differentiable proxy codec to compensate for distortion, allowing it to enhance the quality of decoded content while maintaining the original bitrate, by adding alterations to the source content before encoding.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If existing codecs are used to compress content, then the compression process is fast and compatible with current standards, but visually undesirable artifacts are introduced and compression quality deteriorates
Solution Approach 1:
The pre-processor applies modifications to the source video content before encoding by the existing codec. This preliminary action compensates for expected compression artifacts, allowing the content to maintain higher quality after compression without changing the codec standard itself.
Solution Approach 2:
The system embraces the inevitability of compression artifacts and uses machine learning models to predict and counteract them. By training the pre-processor on compressed content, it learns to add compensatory information that becomes beneficial after the codec introduces its expected distortions.
2Manufacturing precision
If new codec standards are developed to improve compression quality, then compression performance improves, but development time increases and implementation is delayed
Solution Approach 1:
The pre-processor acts as an intermediary layer between the source content and the existing codec. It prepares the content in advance to compensate for codec limitations, effectively bridging the quality gap without requiring new codec standards or lengthy development cycles.
Solution Approach 2:
Rather than waiting for new standards to be developed and implemented, the system performs preliminary processing of the video content to pre-compensate for compression artifacts. This allows immediate quality improvement without the time investment required for standard development.
3Manufacturing precision
If a pre-processor is trained using a differentiable proxy codec to compensate for distortion, then the quality of decoded content improves, but the system complexity increases
Solution Approach 1:
The patent replaces traditional mechanical or algorithmic video processing methods with a machine learning-based pre-processor. The differentiable proxy codec and neural network model substitute for conventional compression optimization approaches, enabling automatic learning of compensation strategies without manual tuning of complex parameters.
Data Source
AI summary
In some embodiments, a method receives source content. A pre-processor pre-processes the source content to output pre-processed source content. The pre-processor includes a first parameter that is trained based on a differentiable proxy codec, and a calculated adjustment to a second parameter of the differentiable proxy codec is used to train the first parameter of the pre-processor. The method encodes the pre-processed source content into compressed pre-processed source content. The compressed pre-processed source content is output.


