|
""" |
|
Modified from https://github.com/mlomnitz/DiffJPEG |
|
|
|
For images not divisible by 8 |
|
https://dsp.stackexchange.com/questions/35339/jpeg-dct-padding/35343#35343 |
|
""" |
|
import itertools |
|
import numpy as np |
|
import torch |
|
import torch.nn as nn |
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from torch.nn import functional as F |
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|
|
|
|
y_table = np.array( |
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[[16, 11, 10, 16, 24, 40, 51, 61], [12, 12, 14, 19, 26, 58, 60, 55], [14, 13, 16, 24, 40, 57, 69, 56], |
|
[14, 17, 22, 29, 51, 87, 80, 62], [18, 22, 37, 56, 68, 109, 103, 77], [24, 35, 55, 64, 81, 104, 113, 92], |
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[49, 64, 78, 87, 103, 121, 120, 101], [72, 92, 95, 98, 112, 100, 103, 99]], |
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dtype=np.float32).T |
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y_table = nn.Parameter(torch.from_numpy(y_table)) |
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c_table = np.empty((8, 8), dtype=np.float32) |
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c_table.fill(99) |
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c_table[:4, :4] = np.array([[17, 18, 24, 47], [18, 21, 26, 66], [24, 26, 56, 99], [47, 66, 99, 99]]).T |
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c_table = nn.Parameter(torch.from_numpy(c_table)) |
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|
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def diff_round(x): |
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""" Differentiable rounding function |
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""" |
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return torch.round(x) + (x - torch.round(x))**3 |
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|
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def quality_to_factor(quality): |
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""" Calculate factor corresponding to quality |
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Args: |
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quality(float): Quality for jpeg compression. |
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|
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Returns: |
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float: Compression factor. |
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""" |
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if quality < 50: |
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quality = 5000. / quality |
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else: |
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quality = 200. - quality * 2 |
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return quality / 100. |
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|
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class RGB2YCbCrJpeg(nn.Module): |
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""" Converts RGB image to YCbCr |
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""" |
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|
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def __init__(self): |
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super(RGB2YCbCrJpeg, self).__init__() |
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matrix = np.array([[0.299, 0.587, 0.114], [-0.168736, -0.331264, 0.5], [0.5, -0.418688, -0.081312]], |
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dtype=np.float32).T |
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self.shift = nn.Parameter(torch.tensor([0., 128., 128.])) |
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self.matrix = nn.Parameter(torch.from_numpy(matrix)) |
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|
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def forward(self, image): |
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""" |
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Args: |
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image(Tensor): batch x 3 x height x width |
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Returns: |
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Tensor: batch x height x width x 3 |
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""" |
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image = image.permute(0, 2, 3, 1) |
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result = torch.tensordot(image, self.matrix, dims=1) + self.shift |
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return result.view(image.shape) |
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|
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class ChromaSubsampling(nn.Module): |
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""" Chroma subsampling on CbCr channels |
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""" |
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def __init__(self): |
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super(ChromaSubsampling, self).__init__() |
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|
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def forward(self, image): |
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""" |
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Args: |
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image(tensor): batch x height x width x 3 |
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|
|
Returns: |
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y(tensor): batch x height x width |
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cb(tensor): batch x height/2 x width/2 |
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cr(tensor): batch x height/2 x width/2 |
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""" |
|
image_2 = image.permute(0, 3, 1, 2).clone() |
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cb = F.avg_pool2d(image_2[:, 1, :, :].unsqueeze(1), kernel_size=2, stride=(2, 2), count_include_pad=False) |
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cr = F.avg_pool2d(image_2[:, 2, :, :].unsqueeze(1), kernel_size=2, stride=(2, 2), count_include_pad=False) |
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cb = cb.permute(0, 2, 3, 1) |
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cr = cr.permute(0, 2, 3, 1) |
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return image[:, :, :, 0], cb.squeeze(3), cr.squeeze(3) |
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|
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class BlockSplitting(nn.Module): |
|
""" Splitting image into patches |
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""" |
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def __init__(self): |
|
super(BlockSplitting, self).__init__() |
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self.k = 8 |
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|
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def forward(self, image): |
|
""" |
|
Args: |
|
image(tensor): batch x height x width |
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|
|
Returns: |
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Tensor: batch x h*w/64 x h x w |
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""" |
|
height, _ = image.shape[1:3] |
|
batch_size = image.shape[0] |
|
image_reshaped = image.view(batch_size, height // self.k, self.k, -1, self.k) |
|
image_transposed = image_reshaped.permute(0, 1, 3, 2, 4) |
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return image_transposed.contiguous().view(batch_size, -1, self.k, self.k) |
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|
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|
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class DCT8x8(nn.Module): |
|
""" Discrete Cosine Transformation |
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""" |
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|
|
def __init__(self): |
|
super(DCT8x8, self).__init__() |
|
tensor = np.zeros((8, 8, 8, 8), dtype=np.float32) |
|
for x, y, u, v in itertools.product(range(8), repeat=4): |
|
tensor[x, y, u, v] = np.cos((2 * x + 1) * u * np.pi / 16) * np.cos((2 * y + 1) * v * np.pi / 16) |
|
alpha = np.array([1. / np.sqrt(2)] + [1] * 7) |
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self.tensor = nn.Parameter(torch.from_numpy(tensor).float()) |
|
self.scale = nn.Parameter(torch.from_numpy(np.outer(alpha, alpha) * 0.25).float()) |
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|
|
def forward(self, image): |
|
""" |
|
Args: |
|
image(tensor): batch x height x width |
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|
|
Returns: |
|
Tensor: batch x height x width |
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""" |
|
image = image - 128 |
|
result = self.scale * torch.tensordot(image, self.tensor, dims=2) |
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result.view(image.shape) |
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return result |
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|
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class YQuantize(nn.Module): |
|
""" JPEG Quantization for Y channel |
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|
|
Args: |
|
rounding(function): rounding function to use |
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""" |
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|
|
def __init__(self, rounding): |
|
super(YQuantize, self).__init__() |
|
self.rounding = rounding |
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self.y_table = y_table |
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|
|
def forward(self, image, factor=1): |
|
""" |
|
Args: |
|
image(tensor): batch x height x width |
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|
|
Returns: |
|
Tensor: batch x height x width |
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""" |
|
if isinstance(factor, (int, float)): |
|
image = image.float() / (self.y_table * factor) |
|
else: |
|
b = factor.size(0) |
|
table = self.y_table.expand(b, 1, 8, 8) * factor.view(b, 1, 1, 1) |
|
image = image.float() / table |
|
image = self.rounding(image) |
|
return image |
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|
|
|
|
class CQuantize(nn.Module): |
|
""" JPEG Quantization for CbCr channels |
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|
|
Args: |
|
rounding(function): rounding function to use |
|
""" |
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|
|
def __init__(self, rounding): |
|
super(CQuantize, self).__init__() |
|
self.rounding = rounding |
|
self.c_table = c_table |
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|
|
def forward(self, image, factor=1): |
|
""" |
|
Args: |
|
image(tensor): batch x height x width |
|
|
|
Returns: |
|
Tensor: batch x height x width |
|
""" |
|
if isinstance(factor, (int, float)): |
|
image = image.float() / (self.c_table * factor) |
|
else: |
|
b = factor.size(0) |
|
table = self.c_table.expand(b, 1, 8, 8) * factor.view(b, 1, 1, 1) |
|
image = image.float() / table |
|
image = self.rounding(image) |
|
return image |
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|
|
|
|
class CompressJpeg(nn.Module): |
|
"""Full JPEG compression algorithm |
|
|
|
Args: |
|
rounding(function): rounding function to use |
|
""" |
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|
|
def __init__(self, rounding=torch.round): |
|
super(CompressJpeg, self).__init__() |
|
self.l1 = nn.Sequential(RGB2YCbCrJpeg(), ChromaSubsampling()) |
|
self.l2 = nn.Sequential(BlockSplitting(), DCT8x8()) |
|
self.c_quantize = CQuantize(rounding=rounding) |
|
self.y_quantize = YQuantize(rounding=rounding) |
|
|
|
def forward(self, image, factor=1): |
|
""" |
|
Args: |
|
image(tensor): batch x 3 x height x width |
|
|
|
Returns: |
|
dict(tensor): Compressed tensor with batch x h*w/64 x 8 x 8. |
|
""" |
|
y, cb, cr = self.l1(image * 255) |
|
components = {'y': y, 'cb': cb, 'cr': cr} |
|
for k in components.keys(): |
|
comp = self.l2(components[k]) |
|
if k in ('cb', 'cr'): |
|
comp = self.c_quantize(comp, factor=factor) |
|
else: |
|
comp = self.y_quantize(comp, factor=factor) |
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|
|
components[k] = comp |
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|
|
return components['y'], components['cb'], components['cr'] |
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|
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|
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|
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|
|
|
class YDequantize(nn.Module): |
|
"""Dequantize Y channel |
|
""" |
|
|
|
def __init__(self): |
|
super(YDequantize, self).__init__() |
|
self.y_table = y_table |
|
|
|
def forward(self, image, factor=1): |
|
""" |
|
Args: |
|
image(tensor): batch x height x width |
|
|
|
Returns: |
|
Tensor: batch x height x width |
|
""" |
|
if isinstance(factor, (int, float)): |
|
out = image * (self.y_table * factor) |
|
else: |
|
b = factor.size(0) |
|
table = self.y_table.expand(b, 1, 8, 8) * factor.view(b, 1, 1, 1) |
|
out = image * table |
|
return out |
|
|
|
|
|
class CDequantize(nn.Module): |
|
"""Dequantize CbCr channel |
|
""" |
|
|
|
def __init__(self): |
|
super(CDequantize, self).__init__() |
|
self.c_table = c_table |
|
|
|
def forward(self, image, factor=1): |
|
""" |
|
Args: |
|
image(tensor): batch x height x width |
|
|
|
Returns: |
|
Tensor: batch x height x width |
|
""" |
|
if isinstance(factor, (int, float)): |
|
out = image * (self.c_table * factor) |
|
else: |
|
b = factor.size(0) |
|
table = self.c_table.expand(b, 1, 8, 8) * factor.view(b, 1, 1, 1) |
|
out = image * table |
|
return out |
|
|
|
|
|
class iDCT8x8(nn.Module): |
|
"""Inverse discrete Cosine Transformation |
|
""" |
|
|
|
def __init__(self): |
|
super(iDCT8x8, self).__init__() |
|
alpha = np.array([1. / np.sqrt(2)] + [1] * 7) |
|
self.alpha = nn.Parameter(torch.from_numpy(np.outer(alpha, alpha)).float()) |
|
tensor = np.zeros((8, 8, 8, 8), dtype=np.float32) |
|
for x, y, u, v in itertools.product(range(8), repeat=4): |
|
tensor[x, y, u, v] = np.cos((2 * u + 1) * x * np.pi / 16) * np.cos((2 * v + 1) * y * np.pi / 16) |
|
self.tensor = nn.Parameter(torch.from_numpy(tensor).float()) |
|
|
|
def forward(self, image): |
|
""" |
|
Args: |
|
image(tensor): batch x height x width |
|
|
|
Returns: |
|
Tensor: batch x height x width |
|
""" |
|
image = image * self.alpha |
|
result = 0.25 * torch.tensordot(image, self.tensor, dims=2) + 128 |
|
result.view(image.shape) |
|
return result |
|
|
|
|
|
class BlockMerging(nn.Module): |
|
"""Merge patches into image |
|
""" |
|
|
|
def __init__(self): |
|
super(BlockMerging, self).__init__() |
|
|
|
def forward(self, patches, height, width): |
|
""" |
|
Args: |
|
patches(tensor) batch x height*width/64, height x width |
|
height(int) |
|
width(int) |
|
|
|
Returns: |
|
Tensor: batch x height x width |
|
""" |
|
k = 8 |
|
batch_size = patches.shape[0] |
|
image_reshaped = patches.view(batch_size, height // k, width // k, k, k) |
|
image_transposed = image_reshaped.permute(0, 1, 3, 2, 4) |
|
return image_transposed.contiguous().view(batch_size, height, width) |
|
|
|
|
|
class ChromaUpsampling(nn.Module): |
|
"""Upsample chroma layers |
|
""" |
|
|
|
def __init__(self): |
|
super(ChromaUpsampling, self).__init__() |
|
|
|
def forward(self, y, cb, cr): |
|
""" |
|
Args: |
|
y(tensor): y channel image |
|
cb(tensor): cb channel |
|
cr(tensor): cr channel |
|
|
|
Returns: |
|
Tensor: batch x height x width x 3 |
|
""" |
|
|
|
def repeat(x, k=2): |
|
height, width = x.shape[1:3] |
|
x = x.unsqueeze(-1) |
|
x = x.repeat(1, 1, k, k) |
|
x = x.view(-1, height * k, width * k) |
|
return x |
|
|
|
cb = repeat(cb) |
|
cr = repeat(cr) |
|
return torch.cat([y.unsqueeze(3), cb.unsqueeze(3), cr.unsqueeze(3)], dim=3) |
|
|
|
|
|
class YCbCr2RGBJpeg(nn.Module): |
|
"""Converts YCbCr image to RGB JPEG |
|
""" |
|
|
|
def __init__(self): |
|
super(YCbCr2RGBJpeg, self).__init__() |
|
|
|
matrix = np.array([[1., 0., 1.402], [1, -0.344136, -0.714136], [1, 1.772, 0]], dtype=np.float32).T |
|
self.shift = nn.Parameter(torch.tensor([0, -128., -128.])) |
|
self.matrix = nn.Parameter(torch.from_numpy(matrix)) |
|
|
|
def forward(self, image): |
|
""" |
|
Args: |
|
image(tensor): batch x height x width x 3 |
|
|
|
Returns: |
|
Tensor: batch x 3 x height x width |
|
""" |
|
result = torch.tensordot(image + self.shift, self.matrix, dims=1) |
|
return result.view(image.shape).permute(0, 3, 1, 2) |
|
|
|
|
|
class DeCompressJpeg(nn.Module): |
|
"""Full JPEG decompression algorithm |
|
|
|
Args: |
|
rounding(function): rounding function to use |
|
""" |
|
|
|
def __init__(self, rounding=torch.round): |
|
super(DeCompressJpeg, self).__init__() |
|
self.c_dequantize = CDequantize() |
|
self.y_dequantize = YDequantize() |
|
self.idct = iDCT8x8() |
|
self.merging = BlockMerging() |
|
self.chroma = ChromaUpsampling() |
|
self.colors = YCbCr2RGBJpeg() |
|
|
|
def forward(self, y, cb, cr, imgh, imgw, factor=1): |
|
""" |
|
Args: |
|
compressed(dict(tensor)): batch x h*w/64 x 8 x 8 |
|
imgh(int) |
|
imgw(int) |
|
factor(float) |
|
|
|
Returns: |
|
Tensor: batch x 3 x height x width |
|
""" |
|
components = {'y': y, 'cb': cb, 'cr': cr} |
|
for k in components.keys(): |
|
if k in ('cb', 'cr'): |
|
comp = self.c_dequantize(components[k], factor=factor) |
|
height, width = int(imgh / 2), int(imgw / 2) |
|
else: |
|
comp = self.y_dequantize(components[k], factor=factor) |
|
height, width = imgh, imgw |
|
comp = self.idct(comp) |
|
components[k] = self.merging(comp, height, width) |
|
|
|
image = self.chroma(components['y'], components['cb'], components['cr']) |
|
image = self.colors(image) |
|
|
|
image = torch.min(255 * torch.ones_like(image), torch.max(torch.zeros_like(image), image)) |
|
return image / 255 |
|
|
|
|
|
|
|
|
|
|
|
class DiffJPEG(nn.Module): |
|
"""This JPEG algorithm result is slightly different from cv2. |
|
DiffJPEG supports batch processing. |
|
|
|
Args: |
|
differentiable(bool): If True, uses custom differentiable rounding function, if False, uses standard torch.round |
|
""" |
|
|
|
def __init__(self, differentiable=True): |
|
super(DiffJPEG, self).__init__() |
|
if differentiable: |
|
rounding = diff_round |
|
else: |
|
rounding = torch.round |
|
|
|
self.compress = CompressJpeg(rounding=rounding) |
|
self.decompress = DeCompressJpeg(rounding=rounding) |
|
|
|
def forward(self, x, quality): |
|
""" |
|
Args: |
|
x (Tensor): Input image, bchw, rgb, [0, 1] |
|
quality(float): Quality factor for jpeg compression scheme. |
|
""" |
|
factor = quality |
|
if isinstance(factor, (int, float)): |
|
factor = quality_to_factor(factor) |
|
else: |
|
for i in range(factor.size(0)): |
|
factor[i] = quality_to_factor(factor[i]) |
|
h, w = x.size()[-2:] |
|
h_pad, w_pad = 0, 0 |
|
|
|
if h % 16 != 0: |
|
h_pad = 16 - h % 16 |
|
if w % 16 != 0: |
|
w_pad = 16 - w % 16 |
|
x = F.pad(x, (0, w_pad, 0, h_pad), mode='constant', value=0) |
|
|
|
y, cb, cr = self.compress(x, factor=factor) |
|
recovered = self.decompress(y, cb, cr, (h + h_pad), (w + w_pad), factor=factor) |
|
recovered = recovered[:, :, 0:h, 0:w] |
|
return recovered |
|
|
|
|
|
if __name__ == '__main__': |
|
import cv2 |
|
|
|
from basicsr.utils import img2tensor, tensor2img |
|
|
|
img_gt = cv2.imread('test.png') / 255. |
|
|
|
|
|
encode_param = [int(cv2.IMWRITE_JPEG_QUALITY), 20] |
|
_, encimg = cv2.imencode('.jpg', img_gt * 255., encode_param) |
|
img_lq = np.float32(cv2.imdecode(encimg, 1)) |
|
cv2.imwrite('cv2_JPEG_20.png', img_lq) |
|
|
|
|
|
jpeger = DiffJPEG(differentiable=False).cuda() |
|
img_gt = img2tensor(img_gt) |
|
img_gt = torch.stack([img_gt, img_gt]).cuda() |
|
quality = img_gt.new_tensor([20, 40]) |
|
out = jpeger(img_gt, quality=quality) |
|
|
|
cv2.imwrite('pt_JPEG_20.png', tensor2img(out[0])) |
|
cv2.imwrite('pt_JPEG_40.png', tensor2img(out[1])) |
|
|
