Comments (4)
Memory size of an image should be constant, since it is typically stored as an array of pixel values in memory. We need more details on what you are doing.
from compression.
I want to resize the image from 1024X1024 to 256X256, and the file size should be reduced while ensuring high fidelity of the image quality. The format of the image file supports common image formats such as jpg
from compression.
Hi, I don't see what this has to do with TensorFlow Compression. Please be more specific about what specific steps you took, what the outcome was, and how it differed from what you expected.
from compression.
When comparing the original image size and the reconstructed image size, you are actually comparing the original encoding with tensorflow encoding and both have nothing to do with your model, to measure your model compression you need to assume that the image is stored in HWC bytes and divide that by the length of compressed string you get from tfc.PackedTensors().string, this yealds the compression ratio.
from compression.
Related Issues (20)
- what's .tfci file and how to get the real codewords of an image HOT 1
- metagraphs link is outdated HOT 2
- Metagraph error while performing compression HOT 1
- libcudart.so.11.0 file
- Regarding GPU and tf compatibility HOT 1
- INVALID_ARGUMENT error during decompression on GPU HOT 1
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- Use tensorflow compression with tensorflow federated on apple silicon HOT 4
- Running all tests fail in Colab Pro+, Premium, High-Ram environment (A100-SXM4-40GB) HOT 5
- tfci.py recognizing, but not using GPUs HOT 2
- TypeError: pack() missing 1 required positional argument: 'arrays' HOT 1
- How to compress an image by the trained model
- How to build tensorflow-compression package for aarch64?
- Unable to save model
- module 'tensorflow_compression' has no attribute 'SignalConv2D'
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- 安卓
- Could not find variable conv1/gdn_0/reparam_gamma
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from compression.