Vector Quantization Semantic Scholar The density matching property of vector quantization is powerful, especially for identifying the density of large and high dimensioned data. since data points are represented by the index of their closest centroid, commonly occurring data have low error, and rare data high error. In this paper, we develop a deep learning (dl) enabled vector quantized (vq) semantic communication system for image transmission, named vq deepsc.
Vector Quantization Semantic Scholar In this paper, we develop a deep learning (dl) enabled vector quantized (vq) semantic communication system for image transmission, named vq deepsc. Specifically, we propose a convolutional neural network (cnn) based transceiver to extract multi scale semantic features of images and introduce multi scale semantic embedding spaces to perform semantic feature quantization, rendering the data compatible with digital communication systems. Recent studies have employed vector quantization (vq) to enable discrete semantic transmission, yet existing methods neglect channel state information during codebook optimization, leading to suboptimal robustness. The proposed mixture of expert codebooks (meco) is a novel multi task learning framework that leverages vector quantization to design mixture of experts with lightweight codebooks that enables efficient learning across multiple dense prediction tasks such as semantic segmentation and monocular depth estimation.
Vector Quantization Semantic Scholar Recent studies have employed vector quantization (vq) to enable discrete semantic transmission, yet existing methods neglect channel state information during codebook optimization, leading to suboptimal robustness. The proposed mixture of expert codebooks (meco) is a novel multi task learning framework that leverages vector quantization to design mixture of experts with lightweight codebooks that enables efficient learning across multiple dense prediction tasks such as semantic segmentation and monocular depth estimation. The proposed model, called svq, leverages recent advances in unsupervised object centric learning to address this limitation and achieves superior generation performance compared to non semantic vector quantization methods such as vq vae and previous object centric generative models. Recent results obtained in waveform coding of speech with vector quantization are reviewed, with vector quantization appearing to be a suitable coding technique which caters to this dual requirement of effective speech coding. This work revisits vector quantization for image compression by revisiting the vq vae framework and introduces several modifications, and proposes a novel conditional entropy model which improves entropy coding by modelling the co dependencies of the quantized latent codes. This paper proposes a novel end to end digital semantic communication framework based on multi codebook vector quantization (vq), referred to as esc mvq.
Figure 4 From Masked Vector Quantization Semantic Scholar The proposed model, called svq, leverages recent advances in unsupervised object centric learning to address this limitation and achieves superior generation performance compared to non semantic vector quantization methods such as vq vae and previous object centric generative models. Recent results obtained in waveform coding of speech with vector quantization are reviewed, with vector quantization appearing to be a suitable coding technique which caters to this dual requirement of effective speech coding. This work revisits vector quantization for image compression by revisiting the vq vae framework and introduces several modifications, and proposes a novel conditional entropy model which improves entropy coding by modelling the co dependencies of the quantized latent codes. This paper proposes a novel end to end digital semantic communication framework based on multi codebook vector quantization (vq), referred to as esc mvq.
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