power-of-two quantizationlow-complexity algorithmsIn this paper, a low-complexity quantization table is proposed for the baseline JPEG encoder. The proposed scheme does not require any multiplications or additions; only bit-shift operations are involved. The computational complexity should be drastically...
Three image compression schemes based on vector quantization are proposed in this paper. The block similarity property among neighboring image blocks is exploited in these schemes to cut down the bit rate of the vector quantization scheme. For the first scheme, the correlation among the encoded ...
[1.58-bit FLUX]: We present 1.58-bit FLUX, the first successful approach to quantizing the state-of-the-art text-to-image generation model, FLUX.1-dev, using 1.58-bit weights (i.e., values in {-1, 0, +1}) while maintaining comparable performance for generating 1024 x 1024 images. ...
Fork 0 Star 1 A repository that shares tuning results of trained models generated by TensorFlow / Keras. Post-training quantization (Weight Quantization, Integer Quantization, Full Integer Quantization, Float16 Quantization), Quantization-aware training. TensorFlow Lite. OpenVINO. CoreML. TensorFlow.js...
0-CHm-1, and then puts them together in a frame and sends the frame evaluates specific properties of the respective channel signals at a time and allocates bits adaptively to the quantization of the respective channels CH0-CHm-1 based on the evaluation result so that quantization errors of...
One-bit quantization is a method of representing bandlimited signals by 卤1 sequences that are computed from regularly spaced samples of these signals; as the sampling density , convolving these one-bit sequences with appropriately chosen filters produces increasingly close approximations of the ...
We further show that the method also extends to other computer vision architectures and tasks such as object detection.Yong YuanChen ChenXiyuan HuSilong Peng会议论文
An adaptive sigma delta modulator has an input stage, a conventional sigma delta modulator, and adaptation stage, and an output stage. The input stage produces a difference signal representing the difference between an analog input signal and an adaptive signal, the amplitude of the analog input ...
doi:10.1007/s11263-024-02250-0QuantizationComputational imagingDifferentiable quantization searchInformation entropyRecent advances of deep neural networks (DNNs) promote low-level vision applications in real-world scenarios,e.g., image enhancement, dehazing. Nevertheless, DNN-based methods encounter ...
Fractional quantization step sizes for high bit ratesAt high bit rates, the reconstruction error of compressed video is generally proportional to the squared value of quantization step size, such that full quantization step size increments at high bit rates can lead to significant change in the ...