Transform Coding Modified Quantization Eliminating Inverse Quantization
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Solution Overview
Problem
Existing transform coding systems face limitations in fully exploiting perceptual modeling techniques due to the need for side information and inverse quantization, which affects compression ratio and decoding efficiency.
Innovation Solution
A modified quantization approach that determines a range of quantized values for each coefficient based on a perceptual model, allowing code values to be selected within this range to minimize bit emission, without requiring inverse quantization at the decoder, and optimizes entropy codes using probability distributions derived from the quantization process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a sophisticated perceptual model is applied to dynamically compute quantization intervals for each coefficient, then perceptual distortion is reduced and coding efficiency is improved, but the decoder requires side information or recomputation capability which increases system complexity and reduces compression ratio
Solution Approach 1:
The patent precomputes and stores quantization intervals in lookup tables during encoder operation. Instead of requiring the decoder to recompute complex perceptual models or receive side information, the encoder performs the computationally intensive perceptual modeling and quantization interval determination in advance, storing only the essential quantization parameters that the decoder needs for reconstruction.
Solution Approach 2:
The patent creates simplified copies of the perceptual model results in the form of quantization lookup tables that can be easily transmitted and stored. Rather than transmitting or recomputing the full sophisticated perceptual model, the system uses precomputed quantization tables that capture the essential perceptual characteristics needed for both encoding and decoding operations.
2Productivity
If predefined quantization intervals based on a priori information are used, then compression ratio is improved and decoder complexity is reduced, but the system cannot exploit perceptual phenomena beyond those separated by the transform
Solution Approach 1:
The patent dynamically adjusts quantization parameters based on the actual perceptual characteristics of the input signal. Instead of using fixed predefined intervals, the system modifies quantization step sizes and intervals according to the local signal properties and perceptual sensitivity, allowing adaptation to different signal types and content while maintaining efficient compression.
Solution Approach 2:
The patent introduces dynamic quantization where the quantization intervals are adjusted based on the actual signal content and perceptual requirements. The quantization parameters are not static but adapt to the signal characteristics, allowing the system to exploit perceptual phenomena as they appear in the actual input data rather than relying on predetermined assumptions.
3Reliability
If side information is included in the coded bitstream to provide hints about coefficient quantization, then decoder accuracy is improved, but the compression ratio deteriorates due to additional bits
Solution Approach 1:
The patent extracts only the essential quantization information needed for accurate decoding and stores it in compact lookup tables during encoding. Instead of transmitting comprehensive side information about the sophisticated perceptual model, the system extracts and transmits only the critical quantization parameters that the decoder needs to reconstruct the signal accurately.
Solution Approach 2:
The encoder performs preliminary analysis and determination of quantization parameters, storing the results in lookup tables that are then used during encoding. This precomputation allows the system to have accurate quantization information available without needing to transmit extensive side information, as the quantization decisions are already determined and stored.
Data Source
AI summary
A transform coding system and method are disclosed which utilize a modified quantization technique which advantageously foregoes the need for inverse quantization at the decoder. New techniques for optimizing an entropy code for the modified quantizer and for constructing the entropy codes are also disclosed.