Transform Quantization for CNN (Convolutional Neural Network) Compression.Sean I. YoungWang ZheDavid TaubmanBernd Girod
The modified image block is afterward subjected to quantization for eliminating the redundant data, then transformed from a 2D matrix to a 1D vector, which in turn is transformed into an intermediate simple form for further compression using entropy coding. Finally, the Huffman coding converts the...
Therefore, DCT is widely used in the field of image quantization, encoding and compression, such as common JPEG static encoding, MJPEG dynamic encoding and MPEG dynamic encoding and other standards. In the field of computer vision, researchers try to apply it to frequency-domain feature learning ...
Jacob B, Kligys S, Chen B, Zhu M, Tang M, Howard A, Adam H, Kalenichenko D (2018) Quantization and training of neural networks for efficient integer-arithmetic-only inference. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2704–2713 Chen H, Wang ...
such as non-eccentricity pattern noise (NERP) and mean shift (MS) in Table 3, which are luminance-related distortions, and change of color saturation (CCS) in Table 3, color diffusion (CD), denoise (DEN), and quantization (QN) in Table 4, which are color-related distortions. Si...
Joint image compression and encryption using IWT with SPIHT, Kd-tree and chaotic maps Appl. Sci.-Basel. (2018) LinW. et al. An efficient watermarking method based on significant difference of wavelet coefficient quantization IEEE Trans. Multimedia (2008)View more referencesCited by (32) ...
Transform Quantization for CNN Compressiondoi:10.1109/TPAMI.2021.3084839Sean I YoungZhe WangDavid TaubmanBernd Girod
Quantization (signal)Wavelet transform is a powerful tool for multiresolution time-frequency analysis. It has been widely adopted in many image processing tasks, such as denoising, enhancement, fusion, and especially compression. Wavelets lead to the successful image coding standard JPEG-2000. ...
The transform, quantization, and inverse transform are jointly trained to achieve the overall rate-distortion optimization. For the training purpose, we propose to estimate the rate by the l_1-norm of the quantized coefficients. We also explore different combinations of linear/non-linear transform ...
Quantization (signal),Memory management,Bandwidth,Principal component analysis,Covariance matrices,Random access memory,Neural networksConvolutional neural networks (CNNs) achieve state-of-the-art accuracy in a variety of tasks in computer vision and beyond. One of the major obstacles hindering the ...