30.80 kB

(-93.51%)
474.76 kB

Expert mode

Offers more options to compress your image.

Reset parameters

Preprocessing

× 512 × 512

Size (px)

Defines the dimensions of the resulting image.

Trim

Removes any borders or edges of an image which did does not change in color or transparency.

Trim fuzz (%)

Defines a threshold for colors that are not-exactly the same, but should be treated as being the same.

Crop down

Will crop the image at the right and the bottom edge to the next lower width and height which is divisible by eight.

This will lower the file size disproportionately higher in comparison to the pixels that get cropped.

Cover

Resizes the image to the next lower width and height which is divisible by eight.

To avoid a distorted image it is possible you will loose some pixel information at the sides (left/right or top/bottom).

Rotation

Rotates the image clockwise in 90 degree increments.


Brightness

Adjusts the brightness of the resulting image.

Contrast

Adjusts the contrast of the resulting image.


Blurring radius (px)

The Selective Blur is used to blur a background so it can be better compressed.

Contrary to other blur algorithms, the Selective Blur doesn't act on all pixels: blur is applied only if the difference between its value and the value of the surrounding pixels is less than a defined threshold value. So, contrasts are preserved because difference is high on contrast limits.

Blurring threshold (%)

The Selective Blur is used to blur a background so it can be better compressed.

Contrary to other blur algorithms, the Selective Blur doesn't act on all pixels: blur is applied only if the difference between its value and the value of the surrounding pixels is less than a defined threshold value. So, contrasts are preserved because difference is high on contrast limits.


Keep metadata

Keeps the meta data (EXIF, IPTC, XMP) of the original image including ICC color profiles.

Compression

Colorspace

Choose »Grayscale« if you want to discard all color information in the resulting JPEG. Results in smaller file size.

Btw. Photoshop creates RGB JPEGs from grayscale images.

More in my article Finally understanding JPG (Grayscale JPG)
and at https://en.wikipedia.org/wiki/Color_space.

Chroma subsampling

Chroma subsampling is the practice of encoding images by implementing less resolution for color information than for brightness information, taking advantage of the human visual system‘s lower acuity for color differences than for brightness (from Wikipedia).

4:2:0
» smaller file size
Breaks the image into 2x2 pixel blocks and only stores the average color information for each 2x2 pixel group (default).
4:2:2
The horizontal chroma resolution is reduced by half.
4:4:4
» bigger file size
The color information of every pixel will be stored.

More in my article Finally understanding JPG (#Chroma Subsampling)
and at https://en.wikipedia.org/wiki/Chroma_subsampling.


Brightness quality

Defines the quality for the brightness channel. Low quality results in more blocking artifacts.

More in my article
Finally understanding JPG (#Quality vs quantization tables).

and in this video:

JPEG Quality in a nutshell

Color quality

Defines the quality for the color channels. Low quality results in wrong colors. Most of the time you can choose a value lower than the value of the brightness quality.

More in my article
Finally understanding JPG (#Quality vs quantization tables).

and in this video:

JPEG Quality in a nutshell


Amount of details (%)

Determines how many details are visible in the resulting image. Influences the quantization tables directly.

The graph shows all 64 values of a quantization table lined up in a row, following the zigzag pattern of the JPEG specification. On the X-axis, the first value therefore corresponds to the DC of the quantization table, followed by the AC values for the details of the image.

The gray line shows the values for a calculated quantization table (based on Appendix I of the JPEG specification) of a selected quality value, while the blue line shows the values of the actually used quantization table, which differs from the gray line if the "Amount of Details" slider was used. In this case, the quantization values for the detailed areas of the image are larger and thus the image is less detailed. To make it easier to see that larger values lead to a decrease in quality, the Y-axis has been reversed.

Practically this means: If you want to create the smallest possible image with good quality, first decrease the quality so far that you don't see any block artifacts anymore. Block artifacts are the most obvious and disturbing artifacts, so they should be avoided in the first place. Then reduce the amount of details as much as you find it acceptable.

Amount of details exponent

Influences how much the "Amount of Details" detail reduction is amplified for more detailed image regions (AC patterns).


Brightness quantization table
  • Current table at Q 75 with details at Q 75
  • Standard table at Q 75
Show as table
865812202631
6671013293028
7781220293528
79111526444031
911192834555239
1218283241525746
2532394452616051
3646484956505250
Color quantization table
  • Current table at Q 50 with details at Q 50
  • Standard table at Q 50
Show as table
1718244799999999
1821266699999999
2426569999999999
4766999999999999
9999999999999999
9999999999999999
9999999999999999
9999999999999999

Structure

Progressive
Image information will be stored from less details to more details. So a big image will be instantly visible but in low quality (default).
Baseline
Pixel information will be stored sequentially in the JPEG file. So a big image will get visible step by step in full resolution.
JPEG < 10 kB
Baseline (75% chance of smaller image)
JPEG > 10 kB
Progressive (94% chance of smaller image)

More in my article Finally understanding JPG (#Structure of the file).

Photoshop compatible

Allows to ensure the compatibility with Adobe Photoshop that cannot read the generated JPEGs. Results in a slighly larger file size.

Useful for photographers who need to open the JPEGs in Photoshop at a later time.


Use selective quality

Allows to have two different quality settings for different parts of the image.


Create mask

Brightness quality (%)

Defines the quality for the brightness channel. Low quality results in more blocking artifacts.


Use transparency

Keeps transparency. This is only possible if you use this image in an HTML environment because you have to add javascript code.

#

Background color

Defines the background color if the transparency of the JPEG is not kept. Set the color in a hexadecimal format as you would do in e.g. in CSS.

Very useful if you have a transparent product image and want to flatten it on a defined background color.


Trellis Quantization

Trellis quantization is an adaptive quantization technique that select the set of levels in a transform block that minimises a rate-distortion metric.

Yes
Softer image, smaller file size (default).
No
Sharper image, bigger file size.

More at https://en.wikipedia.org/wiki/Trellis_quantization

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VideoJPEG quality in a nutshell

Screenshot of a man presenting the topic 'JPEG Quality in a nutshell'

Ben from Compress-Or-Die explains the two quality sliders in the JPEG compressor, their meaning, and how the human eye perceives brightness and color information differently.

He also discusses the quantization table, which determines how much an image is compressed, and how the formula used to calculate the table can differ between programs or online services.

6 min view

View video

You want to learn about JPEG?

Learn more about JPEG compression in my article Finally understanding JPEG.

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News
Christoph Erdmann
Image compression with AI
2023-06-25

Wow! Google researchers have now proposed a new method that combines a standard autoencoder with a diffusion process to recover and add fine details discarded by the autoencoder. Interesting to see the possibilities AI opens up when compressing images.

How Should We Store AI Images? Google Researchers Propose an Image Compression Method Using Score-based Generative Models - MarkTechPost
MarkTechPostA year ago, generating realistic images with AI was a dream. We were impressed by seeing generated faces that resemble real ones, despite the majority of outputs having three eyes, two noses, etc. However, things changed quite rapidly with the release of diffusion models. Nowadays, it is difficult to distinguish an AI-generated image from a […]

Check out our Reddit channel if you want to comment on the news.