Gain-Shape Vector Coding With Adaptive Segment Bit Allocation
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Solution Overview
Problem
Existing gain-shape vector quantization methods face challenges in determining a suitable vector length, leading to increased dynamics handling by the shape quantizer and overhead in gain coding, which affects the bit allocation and perceived quality in audio codecs.
Innovation Solution
The method analyzes the target vector to determine an optimal number of segments for efficient gain-shape vector encoding and decoding, adjusting the number of splits based on energy variations and bit budget constraints to allocate bits effectively between gain and shape coding.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the vector length is increased, then the shape quantizer can handle larger variations within the vector, but the overhead for gain coding increases and fewer bits are left for shape coding
Solution Approach 1:
The target vector is divided into multiple segments, and the encoding process is performed separately for each segment. This segmentation allows the system to handle larger variations within the vector by processing each segment independently, while controlling the gain coding overhead through selective application of gain-shape VQ only to segments that require it.
Solution Approach 2:
The method dynamically determines the number of segments and applies gain-shape VQ selectively based on the characteristics of each segment. The decision to apply gain-shape VQ to a segment is made dynamically based on whether the segment contains significant energy variations, allowing adaptive bit allocation between gain and shape coding.
2Stability of the object's composition
If the number of segments is increased, then the dynamics within the vector are reduced, but the overhead for gain coding increases
Solution Approach 1:
Different encoding strategies are applied to different segments based on their local characteristics. Segments with significant energy variations are encoded using gain-shape VQ, while segments without significant variations are handled differently. This local quality approach ensures that gain coding overhead is incurred only where necessary, rather than uniformly across all segments.
Solution Approach 2:
The method applies gain-shape VQ to only those segments that require it, rather than applying it to all segments. This partial action approach reduces the overall gain coding overhead while still capturing the necessary dynamics in the segments where they exist.
3Ease of manufacture
If a fixed band structure is used, then the encoding process is simplified, but the structure cannot adapt to changes in the target vector
Solution Approach 1:
The band structure is made dynamic by allowing the number of segments and their boundaries to be determined based on the characteristics of the target vector. The encoder analyzes the energy distribution in the target vector and adjusts the segmentation accordingly, enabling adaptation to changes in the target vector while maintaining a relatively simple encoding process.
Solution Approach 2:
The method changes the parameters of the band structure (number of segments, segment boundaries) based on the energy distribution characteristics of the target vector. This parameter adaptation allows the fixed band structure to become flexible and responsive to different input signals, improving versatility without significantly complicating the encoding process.
Data Source
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AI summary
The invention relates to an encoder and a decoder and methods therein for supporting split gain shape vector encoding and decoding. The method performed by an encoder, where the encoding of each vector segment is subjected to a constraint related to a maximum number of bits, BMAX, allowed for encoding a vector segment. The method comprises, determining an initial number, Np_init, of segments for a target vector x; and further determining an average number of bits per segment, BAVG, based on a vector bit budget and Np_init. The method further comprises determining a final number of segments to be used, for the vector x, in the gain shape vector encoding, based on energies of the Np_init segments and a difference between BMAX and BAVG. The performing of the method enables an efficient allocation of the bits of the bit budget over the target vector.