HOA Gain Coding Bounds for Random-Access Audio Decoding
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Solution Overview
Problem
Existing HOA compression methods require high bit rates for transmitting HOA representations, and existing compression techniques do not efficiently determine the minimum number of bits needed for representing non-differential gain values without specifying constraints on the value ranges of HOA representations.
Innovation Solution
Establish an inter-relation between the value range of the input HOA representation and the potential maximum gains of signals before gain control processing, determining the minimum number of bits required for encoding non-differential gain values by normalizing the HOA data frame representation and applying specific transformations to the HOA coefficient sequences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If HOA representation is transmitted without compression, then reconstruction accuracy is maintained, but bit rate becomes excessively high
Solution Approach 1:
The HOA representation is segmented into differential gain values (changes between frames) and non-differential gain values (absolute gains at frame boundaries). This segmentation allows differential coding for most frames, significantly reducing bit rate while maintaining reconstruction accuracy through proper handling of non-differential values at access points.
Solution Approach 2:
The patent establishes predetermined constraints on the value ranges of HOA representations before compression. By normalizing the HOA data frame representation and determining the minimum number of bits required for non-differential gain values in advance, the system prepares the data structure to enable efficient compression without losing reconstruction capability.
2Productivity
If compression is applied to reduce bit rate, then transmission efficiency improves, but determining minimum bits for non-differential gain values becomes complex without value range constraints
Solution Approach 1:
The patent applies normalization to the HOA data frame representation and establishes constraints on value ranges before compression. This preliminary action defines the maximum possible gain values, which directly determines the minimum number of bits required for non-differential gain values, simplifying the compression process.
Solution Approach 2:
The patent transforms the HOA representation by applying gain control processing and normalizing the data. These parameter changes convert the raw HOA coefficients into a normalized form where the relationship between value range and required bit depth becomes deterministic, simplifying subsequent compression operations.
3Quantity of substance
If differential coding is used for gain values, then bit rate is reduced, but random access capability is lost without non-differential gain values
Solution Approach 1:
The patent segments the gain value coding into differential mode (for continuous playback) and non-differential mode (for random access). Non-differential gain values are transmitted at frame boundaries or access points, enabling independent decoding from any point while differential coding reduces bit rate for sequential playback.
Solution Approach 2:
Non-differential gain values serve as intermediary reference points that enable random access. These absolute gain values at access points allow the decoder to reset and begin decoding from any frame without requiring previous frames, while differential coding handles the majority of frames for efficient transmission.
Data Source
AI summary
When compressing an HOA data frame representation, a gain control (15, 151) is applied for each channel signal before it is perceptually encoded (16). The gain values are transferred in a differential manner as side information. However, for starting decoding of such streamed compressed HOA data frame representation absolute gain values are required, which should be coded with a minimum number of bits. For determining such lowest integer number (βe) of bits the HOA data frame representation (C(k)) is rendered in spatial domain to virtual loudspeaker signals lying on a unit sphere, followed by normalisation of the HOA data frame representation (C(k)). Then the lowest integer number of bits is set to βe=┌log2(┌log2(√{square root over (KMAX)}·O)┐+1)┐.