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Implicitly Selected Transform for AVS3
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AI
DOI：10.1109/TIP.2021.3137659.png)
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Abstract
En 中文
Transform coding removes redundancy by de-correlating the residual data, which plays a crucial role in video coding. Since different transform cores can adapt to different video content and residual data, multiple cores transform has been proposed to improve the coding performance. However, the overhead for representing the transform type is inevitable. This paper intensively studies the statistical characteristics of transform coefficients. Theoretical analysis shows that the implicit method in the Implicitly Selected Transform (IST) is superior to the explicit signalling method. As such, we propose the parity adjustment scheme which seamlessly cooperates with the rate-distortion optimization quantization for IST. Furthermore, we propose two combination methods, size-based and number-based, to optimize IST. Moreover, a restriction region of IST is applied to reduce the complexity. Experimental results on HPM-6.0, which is the reference software of the AVS3 video coding standard, show that our proposed method can achieve 1.76% and 0.76% BD-Rate savings on average under AI and RA configurations, respectively, along with negligible decoding time variations. The comparison results between the proposed method and explicit signalling method illustrate that the proposed method can achieve better coding gain than the latter. Our method has been adopted in AVS3.
Keywords:
Transforms
Image coding
Encoding
Video coding
Quantization (signal)
Optimization
Standards
AVS3
transform coding
implicitly selected transform
quantization
Journal
IF:
13.7
Papers:
1.0W
Citations:
8.4W