tf.image.adjust_jpeg_quality

Adjust jpeg encoding quality of an image.

tf.image.adjust_jpeg_quality(
    image, jpeg_quality, name=None
)

This is a convenience method that converts an image to uint8 representation, encodes it to jpeg with jpeg_quality, decodes it, and then converts back to the original data type.

jpeg_quality must be in the interval [0, 100].

Usage Examples:

x = [[[0.01, 0.02, 0.03],
      [0.04, 0.05, 0.06]],
     [[0.07, 0.08, 0.09],
      [0.10, 0.11, 0.12]]]
x_jpeg = tf.image.adjust_jpeg_quality(x, 75)
x_jpeg.numpy()
array([[[0.00392157, 0.01960784, 0.03137255],
        [0.02745098, 0.04313726, 0.05490196]],
       [[0.05882353, 0.07450981, 0.08627451],
        [0.08235294, 0.09803922, 0.10980393]]], dtype=float32)

Note that floating point values are expected to have values in the range [0,1) and values outside this range are clipped.

x = [[[1.0, 2.0, 3.0],
      [4.0, 5.0, 6.0]],
    [[7.0, 8.0, 9.0],
      [10.0, 11.0, 12.0]]]
tf.image.adjust_jpeg_quality(x, 75)
<tf.Tensor: shape=(2, 2, 3), dtype=float32, numpy=
array([[[1., 1., 1.],
        [1., 1., 1.]],
       [[1., 1., 1.],
        [1., 1., 1.]]], dtype=float32)>

Note that jpeg_quality 100 is still lossy compresson.

x = tf.constant([[[1, 2, 3],
                  [4, 5, 6]],
                 [[7, 8, 9],
                  [10, 11, 12]]], dtype=tf.uint8)
tf.image.adjust_jpeg_quality(x, 100)
<tf.Tensor: shape(2, 2, 3), dtype=uint8, numpy=
array([[[ 0,  1,  3],
        [ 3,  4,  6]],
       [[ 6,  7,  9],
        [ 9, 10, 12]]], dtype=uint8)>

image 3D image. The size of the last dimension must be None, 1 or 3.
jpeg_quality Python int or Tensor of type int32. jpeg encoding quality.
name A name for this operation (optional).

Adjusted image, same shape and DType as image.

InvalidArgumentError quality must be in [0,100]
InvalidArgumentError image must have 1 or 3 channels