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Either way, providing CSI via limited feedback is always prone to inaccuracies, on the one hand, caused by the quantization process, which is inevitable due to the limited capacity of the feedback links.
In this context, an integer value represents each quantization level used by the quantization process.
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All these blocks are processed by an 8×8 integer transform that was designed in conjunction with the quantization process by using the pre-scaled integer transform (PIT) technique [9], in order to reduce the rounding errors and minimize the complexity of the decoder implementation.
By utilizing constraint information in the quantization process, the proposed method can fully preserve pairwise semantic similarities for binary codes thus leading to better retrieval performance.
This is because we want to benefit from the V/UV signal segmentation to improve the quantization process by better fitting the general trends of voiced or unvoiced LSFs.
In the proposed encoder, the quantization process is performed by two strategies: one coarser and another finer.
The quantization process is similar to the technique used by Li [27], due to fact that the coefficient of lowpass sub-band ranges between 0 ∼255, and Δ is the quantization step.
Higher noise-tolerance can be obtained with the minimum lost information in the quantization process.
The encoding or the quantization process is denoted as.
The quantization process in H.264/AVC is defined as follows: (3).
To improve coding efficiency, the rate-distortion optimized quantization (RDOQ) is often employed, during the quantization process.
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CEO of Professional Science Editing for Scientists @ prosciediting.com