quantization

NeuQuant.js works well when the image width and height are a multiple of 100: 300x300 Otherwise, there is obviously a bug: 299x300 (These were made with this web app .) I'm 90% sure that the bug is in NeuQuant.js. I have made tests using it with jsgif and omggif , and both encoders have the same bug. It is only obvious with photographic images (quantize to 256 colors) when the image size is anything other than a multiple of 100. If you understand neural networks, color quantization, and/or issues with porting AS3 to JS, please take a look. The original porter has abandoned the project, and it

I'm trying to use UINT8 quantization while converting tensorflow model to tflite model: If use post_training_quantize = True , model size is x4 lower then original fp32 model, so I assume that model weights are uint8, but when I load model and get input type via interpreter_aligner.get_input_details()[0]['dtype'] it's float32. Outputs of the quantized model are about the same as original model. converter = tf.contrib.lite.TFLiteConverter.from_frozen_graph( graph_def_file='tflite-models/tf_model.pb', input_arrays=input_node_names, output_arrays=output_node_names) converter.post_training

This is a follow-up of another question by me : Error with 8-bit Quantization in Tensorflow Basically, I would like to install the Tensorflow with 8-bit quantization support. Currently, I installed Tensorflow 0.9 with pip installation method on CentOS 7 machine (without GPU support). I could compile and run the codes as given in Pete Warden's blog post. But, I can't import the functions given in Pete Warden's reply. I would like to add the quantization support. I couldn't find any details about the quantization part in the Tensorflow documentation also. Can anybody share the details on how to

Say I have a float in the range of [0, 1] and I want to quantize and store it in an unsigned byte. Sounds like a no-brainer, but in fact it's quite complicated: The obvious solution looks like this: unsigned char QuantizeFloat(float a) { return (unsigned char)(a * 255.0f); } This works in so far that I get all numbers from 0 to 255, but the distribution of the integers is not even. The function only returns 255 if a is exactly 1.0f . Not a good solution. If I do proper rounding I just shift the problem: unsigned char QuantizeFloat(float a) { return (unsigned char)(a * 255.0f + 0.5f); } Here

So I'm trying to encode some animated gif files in my Java application. I've been using some classes/algorithms found online, but none seem to be working well enough. Right now I'm using this quantize class to reduce the colors of an image down to 256: http://www.java2s.com/Code/Java/2D-Graphics-GUI/Anefficientcolorquantizationalgorithm.htm The problem is, it doesn't seem to be very "smart." If I pass in an image with more than 256 colors, it does reduce the color number, but not very well. (Reds turn blue, etc - very obvious errors like this). Are there any other algorithms/libraries for