Video Encoder Selective Block Filtering for Decoding Complexity
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Solution Overview
Problem
The increased decoding complexity caused by deep learning-based loop filtering tools in video encoding and decoding processes hinders their practical application, as they require significant computational resources without a proportional improvement in video quality.
Innovation Solution
A method where a video encoder determines the filtering gain of each block in a reconstructed video frame and transmits indication information to the decoder, allowing it to only filter blocks with significant filtering gains, thereby reducing decoding complexity and improving efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If deep learning-based loop filtering tools are used in video encoding and decoding, then video quality is improved, but decoding complexity increases significantly
Solution Approach 1:
The video frame is divided into multiple blocks, and filtering is applied selectively to individual blocks based on their characteristics. The encoder determines which blocks require filtering and transmits this information to the decoder, enabling localized filtering operations rather than applying filtering to the entire frame, thus reducing overall decoding complexity while maintaining video quality in critical areas.
Solution Approach 2:
Different filtering operations are applied to different blocks based on their local characteristics. Blocks with significant filtering gains (determined by comparing original and reconstructed block differences) receive filtering, while blocks with minimal gains do not. This localized approach ensures video quality is improved where needed without incurring unnecessary computational complexity in areas where filtering provides little benefit.
2Manufacturing precision
If filtering is applied to all blocks in the reconstructed video frame, then video quality is improved, but computational resources are wasted on blocks with minimal filtering gains
Solution Approach 1:
Instead of applying filtering to all blocks (excessive action), the encoder evaluates each block's filtering gain and applies filtering only to blocks where it provides significant improvement (partial action). Blocks with filtering gains below a threshold are excluded from filtering, eliminating wasted computational resources while maintaining video quality in blocks that benefit from filtering.
Solution Approach 2:
The encoding process automatically identifies which blocks require filtering by evaluating filtering gains, and this information is transmitted to the decoder. The system self-regulates the filtering application based on actual video content characteristics, eliminating the need for manual configuration or uniform filtering approaches that waste computational resources.
Data Source
AI summary
Embodiments of this disclosure provide video encoding and decoding methods and devices relates to artificial intelligence (AI) technologies applied to video encoding and decoding and are implemented to perform video filtering, so that coding blocks with a relatively greater filtering gain may be selected for filtering after reconduction, thereby improving filtering efficiency. An example video encoding method includes: obtaining a reconstructed video frame from encoded data of an encoded video frame, the encoded video frame comprising at least one encoding block; filtering the reconstructed video frame, to obtain a filtering gain of each pixel of the at least one encoding block; for an encoding block in the at least one encoding block, determining, according to a distribution of gains of pixels of the encoding block, whether the encoding block is to be filtered when being decoded at a decoder; and in response to determining that the encoding block is to be filtered when being decoded at a decoder, including into the encoded data of the encoded video frame an indication information for indicating that the encoding block is to be filtered into the encoded data.


