JPEG Image Compression Detection And Quality Factor Detection

Few methods for JPEG Image Compression Detection And Quality Factor Detection

Low Compression	Medium Compression	High Compression

Methods

Split the image into 8x8 blocks.
Compute the DCT coefficients for each block and quantize the frequency components.
Compute the normalized frequencies across all the blocks.
Compute the variance of the high frequency blocks.
If the variance is high, then unclamped high frequency components are present. If the variance is low, then jpeg quanitzed high frequency components are present.
The above observation can be used to classify if a jpeg image is compressed.

Reconstructed Reference	Input	Residual

Compute a reference denoised image using Real-ESRGAN.
Compute the residual image, i.e., difference between reference denoised image and input image.
The residual image contains high-frequency components, artifacts, etc.
The residual image is passed to a resnet model to get classifcation and regression of jpeg-compression and quality factor respectively.
The model is trained with augmenting single, doube jpeg compressions.
Non-aligned double JPEG compression should be implicitly handled as the reference image should produce a constant noise pattern.

We compute the JPEG Quality Factor using a pretrained regressor, Towards Flexible Blind JPEG Artifacts Removal (FBCNN, ICCV 2021) which supports single, doube jpeg compressions with Non-alignment of the quantized matrices in double jpeg compression.
The FBCNN repository is used.

Run get_dataset.sh to download the datasets.

bash get_dataset.sh

Execute dct_validation.py to obtain the validation results stored as csv in outputs/dct.csv

python3 dct_validation.py

Execute dnn_validation.py to obtain the validation results stored as csv in outputs/dnn.csv

python3 dnn_validation.py

Execute fbcnn_validation.py to obtain the validation results stored as csv in outputs/fbcnn.csv

python3 fbcnn_validation.py

To train the DNN model, we run train.py

python3 train.py