Adaptive Reconstruction Levels for Data Compression
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Solution Overview
Problem
Current video encoding standards, such as H.264, use uniform quantization step sizes, which may not optimally balance distortion and coding rate due to assumptions of uniform data distribution, leading to suboptimal rate-distortion performance in lossy data compression.
Innovation Solution
Implementing adaptive reconstruction levels within the quantization process, where each sub-part of the data space has a unique quantization index and adaptive reconstruction level based on the actual data distribution, minimizing joint cost of distortion and transmission rate.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If uniform quantization step sizes are used, then the encoding process is simple and consistent, but the rate-distortion performance is suboptimal due to assumptions of uniform data distribution
Solution Approach 1:
The patent applies local quality by using different reconstruction levels for different quantization indices based on the actual data distribution in each sub-part of the data space. Instead of using a uniform reconstruction approach, the system calculates and stores adaptive reconstruction levels (q_u) for each quantization index, allowing the quantization process to adapt to local characteristics of the data distribution and improve rate-distortion performance.
2Manufacturing precision
If adaptive reconstruction levels are calculated and stored for each sub-part, then rate-distortion performance is improved, but the complexity of the encoding process increases
Solution Approach 1:
The patent applies preliminary action by pre-calculating and storing the adaptive reconstruction levels (q_u) during an initialization phase or offline process. This allows the encoding process to simply look up and use these pre-computed values rather than calculating them in real-time during encoding, thereby reducing the computational complexity of the actual encoding operation while still achieving improved rate-distortion performance.
3Manufacturing precision
If adaptive reconstruction levels are used, then the balance between distortion and coding rate is improved, but more data must be transmitted and stored
Solution Approach 1:
The patent applies parameter changes by modifying the reconstruction levels parameter from fixed uniform values to adaptive values (q_u) that are specific to each quantization index and sub-part of the data space. This parameter adaptation allows the system to achieve better distortion-coding rate balance by matching the reconstruction levels to the actual data distribution characteristics, thereby improving efficiency without requiring excessive additional data transmission.
Data Source
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AI summary
Encoding and decoding methods that perform quantization using adaptive reconstruction levels are presented. The reconstruction levels for a given partitioning of the data space may be selected based upon the distribution of data points within each sub-part defined by the partitioning. In some cases, the adaptive reconstruction levels may be based upon an average of the data points within each sub-part. In some cases, the adaptive reconstruction levels may be selected using a rate-distortion analysis including the quantization distortion associated with the levels versus the data points and the rate associated with transmitting the encoded adaptive reconstruction levels. The methods relate to data compression in a range of applications including audio, images and video.