Transform Domain Filtering for Video Encoding Rate-Distortion
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Solution Overview
Problem
Current video encoding standards, such as H.264, face challenges in achieving optimal rate-distortion performance due to lossy quantization noise, which requires post-processing filtering in the pixel domain, increasing computational complexity.
Innovation Solution
Implementing transform domain filtering, where quantization distortion is addressed in the transform domain before reconstruction, using techniques like discrete cosine transform (DCT) and adaptive filter coefficients to minimize distortion while reducing computational complexity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If post-processing filtering is applied in the pixel domain to remove quantization noise, then distortion is reduced, but computational complexity increases
Solution Approach 1:
The patent inverts the conventional approach by applying filtering in the transform domain (frequency domain) rather than in the pixel domain (spatial domain). The filter is applied to transform coefficients before inverse transformation, which reduces computational complexity while maintaining distortion reduction effectiveness.
Solution Approach 2:
The patent introduces transform domain coefficients as an intermediary between quantization and final reconstruction. By applying the filter in this intermediate transform domain rather than directly in the pixel domain, the system achieves efficient noise removal with reduced computational burden.
2Manufacturing precision
If transform domain filtering is implemented to reduce quantization distortion, then rate-distortion performance is improved, but encoding complexity increases
Solution Approach 1:
The patent applies different filter coefficients to different transform coefficients based on their position and characteristics. By adapting the filter locally to specific coefficient regions rather than applying a uniform filter globally, the system improves rate-distortion performance while controlling encoding complexity through selective filtering.
3Manufacturing precision
If adaptive filter coefficients are used to minimize distortion, then manufacturing precision is improved, but device complexity increases
Solution Approach 1:
The patent changes the parameters of the filter by using adaptive coefficients that vary based on transform coefficient characteristics rather than using fixed coefficients. This allows distortion minimization while managing complexity through parameter adaptation rather than structural complexity.
Data Source
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AI summary
Methods and devices for encoding and decoding data using transform domain filtering are described. The encoder determines a set of transform domain filter coefficients to be applied to a transform domain prediction. The filtering may, in some cases, also apply to transform domain reconstructions. Rate-distortion optimization may be used to determine the optimal filter coefficients on a frame-basis, coding-unit-basis, or other basis. Multiple filters may be developed and communicated from the encoder to the decoder for different combinations of transform block size, coding mode, prediction mode, and texture type. In other cases, the filtering is applied in the pixel-domain to a pixel-domain prediction or a pixel-domain reconstruction of a block of samples.