Bit Sequence Rearrangement for Higher Entropy Encoding Efficiency
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Solution Overview
Problem
Existing data encoding methods face limitations in improving transmission efficiency, particularly for large data sets like image data, as they do not effectively utilize bit probability distributions to rearrange bits for optimal encoding.
Innovation Solution
A method that calculates the probability of bit '1' occurrences in previously encoded data and rearranges bits within new data based on these probabilities for entropy-encoding, using techniques like Huffman or Golomb encoding to enhance encoding efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional Huffman encoding is used without bit rearrangement, then the encoding process is simple, but the encoding efficiency is limited and cannot fully utilize bit probability distributions
Solution Approach 1:
The patent applies preliminary action by rearranging bits before entropy encoding. Specifically, bits are reordered based on their probability of being '1' (calculated from previously encoded data) so that higher probability bits are positioned in lower bit locations. This preliminary bit rearrangement optimizes the input for subsequent Huffman or Golomb encoding, enabling better compression ratios without adding complex encoding algorithms.
Solution Approach 2:
The patent changes the parameter of bit position ordering based on probability distributions. By calculating the probability that each bit position contains a '1' from previously encoded data, and then rearranging current data bits according to these probabilities, the encoding process adapts to the actual data characteristics, improving encoding efficiency for different data types and patterns.
2Productivity
If bits are rearranged based on probability calculations from previously encoded data, then encoding efficiency improves, but the encoding process becomes more complex
Solution Approach 1:
The patent applies local quality by treating different bit positions differently based on their individual probability characteristics. Each bit position is evaluated separately based on the probability of containing a '1', and bits are rearranged to place higher probability bits in specific positions (lower bit locations for Huffman encoding). This localized optimization of bit positions based on their individual properties improves overall encoding efficiency.
3Quantity of substance
If probability-based bit rearrangement is applied, then more data can be transmitted via narrower bandwidth, but additional processing steps are required
Solution Approach 1:
The patent performs preliminary bit rearrangement based on probability calculations from previously encoded data before the actual entropy encoding process. This preliminary action optimizes the bit sequence to match the characteristics of the subsequent Huffman or Golomb encoding algorithm, enabling more efficient compression and allowing greater data quantities to be transmitted through narrower bandwidth channels.
Data Source
AI summary
Provided are methods and apparatuses for encoding data. One method of encoding data includes generating second data by altering locations of bits within first data to be encoded next based on values of bits within at least one piece of previously encoded data; and entropy-encoding the second data. Also provided are methods and apparatuses for decoding data.