Arithmetic Encoder Layout for Unused Code Space Compression
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Solution Overview
Problem
Digital communication systems face inefficiency due to non-integer length code words resulting from channel bandwidth constraints, leading to underutilization of bits, especially when the number of unique frames (n) is not a power of two, causing computational overhead in high-bandwidth, high-fidelity systems.
Innovation Solution
The solution involves splitting multi-bit code words into Most Significant Bit (MSB) and Least Significant Bit (LSB) portions, using Arithmetic Coding to fully utilize LSB bits and redistribute saved bits to MSB portions, thereby addressing inefficiency and reducing computational complexity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If k-bit integers are used to represent each frame when n≠2^k, then the allocated bandwidth is sufficient to transmit the information, but some range of higher valued integers near 2^k would never be sent, causing inefficiency in bit utilization
Solution Approach 1:
The patent segments the code word into two parts: a first part representing the most significant bits and a second part representing the least significant bits. This segmentation allows the first part to utilize the full k-bit range while the second part efficiently encodes the remaining n-2^k possibilities, thereby fully utilizing all k bits without waste.
Solution Approach 2:
The patent applies different encoding strategies to different parts of the code word. The most significant bits use standard k-bit integer encoding while the least significant bits use a specialized encoding scheme tailored to the specific value of n, optimizing each segment for its particular requirements and achieving overall bit efficiency.
2Manufacturing precision
If the number of unique frames n is not a power of two, then the system can accurately represent all possible frames, but the bit allocation becomes inefficient with unused code combinations
Solution Approach 1:
By dividing the code word into two segments with different encoding rules, the patent achieves precise representation of all n frames while avoiding the complexity of designing a completely custom encoding scheme for non-power-of-two values. The segmentation allows use of simple integer encoding for the majority of bits.
Solution Approach 2:
The patent uses partial integer encoding where only the necessary portion of the k-bit space is fully utilized. The first part uses standard integer encoding while the second part uses a reduced encoding scheme, applying encoding actions only where needed rather than uniformly across all bits.
3Measurement precision
If high precision multiplication operations are performed to process a large number of codewords, then the encoding accuracy is maintained, but the computational expense increases significantly
Solution Approach 1:
The patent segments the encoding process into two independent stages: encoding the most significant bits and encoding the least significant bits. This segmentation allows each stage to use simpler, more efficient operations rather than requiring high-precision multiplication across the entire code word, thereby reducing computational expense while maintaining accuracy.
Solution Approach 2:
The patent extracts and handles the least significant bits separately from the most significant bits. By taking out the LSB encoding as a distinct operation, the system avoids the computational burden of high-precision multiplication and uses simpler arithmetic operations tailored to the specific requirements of the non-power-of-two code space.
Data Source
AI summary
An encoder/decoder architecture including an arithmetic encoder that encodes the MSB portions of a Factorial Pulse Coder output, and that encodes an output of a first-level source encoder, e.g., MDCT. Sub-parts (e.g., frequency bands) of portions (e.g., frames) of the signal are sorted in increasing order based on a measure related to signal energy (e.g., signal energy itself). In a system that overlays Arithmetic Encoding on Factorial Pulse coding, the result is bits re-allocated to bands with higher signal energy content, yielding higher signal quality and higher bit utilization efficiency.