Neural Network Post-Processing for Video Encoder Bitstream Concatenation
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Solution Overview
Problem
Current video coding technologies, such as HEVC, require further improvement in encoding and decoding efficiency to achieve better image quality and reduced data amounts.
Innovation Solution
An encoder and decoder system that utilizes a neural network-based post-processing network to concatenate bitstreams, incorporating both local decoded images and supplemental information to generate a reconstructed image, which is then used for prediction encoding, thereby reducing the data amount and enhancing image quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If conventional video coding techniques (HEVC) are used, then encoding and decoding processes are straightforward, but image quality and compression efficiency require further improvement
Solution Approach 1:
A post-processing neural network is introduced as an intermediary component between the conventional codec and the final output. This neural network processes the decoded image and supplemental information to generate a reconstructed image with superior quality, effectively mediating between the limitations of conventional coding and the desire for higher image quality and compression efficiency
2Manufacturing precision
If a neural network-based post-processing system is introduced, then image quality and compression efficiency improve, but system complexity increases
Solution Approach 1:
The system is segmented into distinct functional modules: a conventional codec module for basic encoding/decoding, a supplemental information processing module, and a post-processing neural network module. This segmentation allows each component to specialize in specific tasks, improving overall performance while making the complex system more manageable and implementable
3Manufacturing precision
If supplemental information is encoded and processed separately, then more data is transmitted, but better reconstruction quality is achieved
Solution Approach 1:
The first bitstream from the conventional codec and the second bitstream from supplemental information processing are merged into a single concatenated bitstream. This combining approach allows the system to leverage both conventional compression efficiency and supplemental quality enhancement while presenting a unified data stream for processing
Data Source
AI summary
An encoder includes circuitry and memory. Using the memory, the circuitry: encodes an original image and decodes the original image encoded, to generate a first bitstream and a local decoded image; encodes supplemental information and decodes the encoded supplemental information, to generate a second bitstream and local decoded supplemental information; inputs data based on the local decoded image and the local decoded supplemental information to a post processing network which is a neural network, to cause a reconstructed image to be output from the post processing network, the reconstructed image corresponding to the original image and being to be used to encode a following original image which follows the original image; and concatenates the first bitstream and the second bitstream to generate a concatenated bitstream.