HOA Gain Bit Allocation from Normalized Virtual Loudspeaker Rendering
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Solution Overview
Problem
Existing HOA compression methods do not provide a systematic approach to determine the minimum number of bits required for representing non-differential gain values, which is essential for efficient coding and random access in HOA decompression, due to the lack of constraints on the value ranges of HOA representations.
Innovation Solution
An apparatus that establishes an inter-relation between the value range of the input HOA representation and the potential maximum gains of signals before gain control, determining the minimum number of bits required for encoding non-differential gain values by using a processing method that normalizes the input HOA representation and applies specific constraints on the value ranges.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If HOA representation is compressed using conventional methods without value range constraints, then the compression can be applied to any input, but the number of bits required for representing non-differential gain values is indeterminate and potentially excessive
Solution Approach 1:
The patent applies parameter changes by imposing specific value range constraints on the HOA representation input. By normalizing the input signals to lie within a defined range (e.g., [-1, 1]), the system transforms the indefinite bit requirement into a determinate one. This parameter constraint enables the calculation of a minimum integer number of bits needed for representing non-differential gain values, thereby improving bit rate efficiency while maintaining reliability through the standardized input format
2Loss of information
If the number of bits for non-differential gain values is reduced to improve compression efficiency, then bit rate decreases, but the ability to accurately represent gain values may be compromised
Solution Approach 1:
The patent applies preliminary action by normalizing the HOA input signals before compression. By pre-constraining the signal values to a known range, the system enables deterministic calculation of the minimum bits required for gain value representation. This preliminary normalization ensures that subsequent compression with a determined bit allocation maintains adequate precision, as the dynamic range of gain values is bounded by the preprocessing step
Solution Approach 2:
The patent replaces the mechanical approach of using fixed or excessive bit allocations with a calculated, adaptive bit determination method. Instead of allocating a fixed number of bits regardless of input characteristics, the system substitutes this with a calculation based on the actual value range of the normalized input, thereby optimizing the balance between bit rate and representation accuracy
3Adaptability or versatility
If no value range constraints are imposed on HOA input, then the system is highly adaptable to different inputs, but systematic determination of minimum bits for gain values becomes impossible
Solution Approach 1:
The patent applies universality by creating a standardized input format that serves multiple functions. The value range constraint (normalization) simultaneously enables: (1) systematic bit determination for gain values, (2) improved compression efficiency, (3) consistent performance across different inputs, and (4) simplified decoder design. This single constraint provides multiple benefits, making the system universally applicable while reducing overall complexity
Data Source
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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⌈log2KMAX⋅O⌉+1⌉.