Binary Data Quantization for Predictable Bit Allocation
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Solution Overview
Problem
Existing binary data quantization methods face issues such as unpredictable bit requirements, incorrect rate control, and quality degradation due to extra bits needed for quantization and dead zones, especially in high-resolution and high-frame-rate audio-visual data processing.
Innovation Solution
A method that divides the range of binary data values into 2b+1 subranges with a quantization step size of 2B+1-1, allowing for predictable bit allocation and preserving zero values, using either register-shift or lookup table methods for compression and decompression.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If traditional quantization methods are used to reduce bit depth from B to b bits, then compression ratio is improved, but the number of bits required for coding becomes unpredictable and rate control becomes incorrect
Solution Approach 1:
The quantization process is segmented into distinct operations: determining the quantization step size dq, dividing the value range into subranges, and mapping input values to quantized output values. This segmentation allows precise control over the number of output bits by designing the subrange boundaries to align with b-bit representable values, eliminating unpredictability in bit allocation.
Solution Approach 2:
The invention changes the quantization step size parameter dq to a specific value (2^(B-b)) that ensures the quantized values fit exactly within b bits. By adjusting this parameter, the system achieves predictable bit allocation while maintaining compression efficiency, resolving the contradiction between compression ratio and bit allocation predictability.
2Quantity of substance
If quantization is applied to reduce storage capacity and bandwidth, then data volume is reduced, but quality degradation occurs due to truncation error
Solution Approach 1:
The quantization step size dq is precisely set to 2^(B-b), which creates uniform subranges that optimally balance compression ratio and reconstruction accuracy. This specific parameter choice minimizes truncation error while ensuring the quantized values can be represented in exactly b bits, achieving the best possible precision for the given bit depth.
3Measurement precision
If an extra bit is added to code the quantized value correctly, then precision is maintained, but the compression efficiency is reduced and rate control becomes difficult
Solution Approach 1:
The quantization step size is specifically chosen as dq = 2^(B-b), which ensures that all quantized values fall within the range representable by exactly b bits without requiring an extra bit. This precise parameter selection eliminates the need for additional bits while maintaining correct precision, resolving the contradiction between precision and compression efficiency.
4Manufacturing precision
If high-resolution and high-frame-rate processing is implemented, then image quality is improved, but storage capacity and bandwidth requirements increase significantly
Solution Approach 1:
The quantization process segments the large volume of high-resolution video data into manageable units that can be efficiently compressed. By applying the optimized quantization with dq = 2^(B-b) to each block of coefficients, the system reduces the bit depth from B to b bits while maintaining image quality, achieving significant storage capacity reduction for high-resolution and high-frame-rate content.
Data Source
AI summary
The invention relates to a method for compressing a set of input binary data values x, all coded in a same number B of bits, into a corresponding set of output data values x′, all coded in a smaller number b of bits, obtainable by(i) computing a quantization step size dqdq=2B+12b+1-1(ii) dividing the range of values [0, 2B−1[, into 2b subranges,the first subrange being [0, INT(dq/2)[the ith subrange being [INT((i−3/2)*dq)+1, INT((i−1/2)*dq)[, for i going from 2 to 2b The invention also relates to a method for decompressing data and to applications of said method for compressing/decompressing video data, and to devices for performing these operations.