Find Similar JPG Images (JPEG)

Find similar JPG files of any type and also compare them with any other image format. Visual Similarity Duplicate Image Finder support over 100 popular image formats and also 300 RAW camera formats. It can find exact duplicates regardless of the image format and also similar images that vary in color, crop size, watermarks, and edits. The most popular supported JPEG files are:

• JPEG Bitmap (*.jpg;*.jpeg;*.jpe;*.jfif;*.jif)
• JPEG2000 Files (*.jp2)
• JPEG2000 Code Stream (*.j2k;*.jpc;*.j2c) 

Duplicate Image Finder

Finding similar JPG or any other images is not something that a common duplicate finder application can do. VSDIF performs true image analysis in order to identify similarities between photos stored in different image formats. In order to do this, a tool should be able to decode all the image formats that have to be compared. Read more about which duplicate file finder to choose.

Visual Similarity Duplicate Image Finder was chosen as the best duplicate photo finder. It provides the highest precision, performance and can compare millions of photos and terabytes of data. It is the number one choice of professional photographers as it can find duplicate photos in Lightroom too.

Due to the fact that it can handle large photo libraries, it is used to organize corporate image databases. The support for DICOM images is the reason why it is used in the medical industry and also in medical research laboratories.

How to Find Similar JPG Images (JPEG)

Finding similar JPG images using VSDIF is as simple as finding any other image formats. Follow the simple steps below:

1. Add the folders that contain your JPG files to the folder list on the right.
2. Specify how similar the listed JPG files should be by altering the similarity percentage.
3. Press the “Start scan” button.
4. After the scan is complete the tool will group together all similar and duplicate JPG files.
5. Review the results and tick the JPG files you want to remove.
6. Use the Delete. Move or Copy actions on the chosen files.

Finding and removing duplicate and similar JPEG files is as simple as that. By removing duplicate files you will save disk space. You will also improve the performance of your computer and maintain an organized image library.

Find Similar Images in Any Image Format

The more image formats a tool supports the more duplicates it can find. If a tool can not decode a certain image format it can not view the photo that it contains and it can not analyze it.

Download Duplicate Photo Finder and organize your image library.

Now that you know how to find similar JPG files, you can learn more about the JPEG file format below. The information is detailed and complete

Comprehensive JPEG File Format Information

In computing, JPEG (/ˈdʒeɪpɛɡ/ JAY-peg)[1] (seen most often with the .jpg extension) is a commonly used method of lossy compression for digital images, particularly for those images produced by digital photography. You can adjust the degree of compression. That allows you a selectable tradeoff between storage size and image quality. JPEG typically achieves 10:1 compression with little perceptible loss in image quality.

A number of image file formats use JPEG compression. Undoubtedly, JPEG/Exif is the most common image format. Therefore, Digital cameras and other photographic image capture devices use it. Along with JPEG/JFIF, it is the most common format for storing and transmitting photographic images on the World Wide Web. However, many people do not distinguish those format variations and simply call them JPEG.

The term “JPEG” is an acronym for the Joint Photographic Experts Group, which created the standard. The MIME media type for JPEG is image/jpeg ( RFC 1341). An exception is Internet Explorer, which provides a MIME type of image/pjpeg when uploading JPEG images.

JPEG/JFIF supports a maximum image size of 65535×65535.

The JPEG standard

The name “JPEG” stands for Joint Photographic Experts Group, the name of the committee that created the JPEG standard and also other still pictures coding standards. The “Joint” stood for ISO TC97 WG8 and CCITT SGVIII. In 1987 ISO TC 97 became ISO/IEC JTC1 and in 1992 CCITT became ITU-T. Currently on the JTC1 side JPEG is one of two sub-groups of ISO/IEC Joint Technical Committee 1, Subcommittee 29, Working Group 1 (ISO/IEC JTC 1/SC 29/WG 1) – titled as Coding of still pictures. On the ITU-T side, ITU-T SG16 is the respective body. The original JPEG group was organized in 1986, issuing the first JPEG standard in 1992, which was approved in September 1992 as ITU-T Recommendation T.81 and in 1994 as ISO/IEC 10918-1.

The JPEG standard specifies the codec. It defines how an image is compressed into a stream of bytes and decompressed back into an image. Nevertheless, it does not define the file format used to contain that stream. The Exif and JFIF standards define the commonly used file formats for the interchange of JPEG-compressed images.

JPEG standards are formally named Information technology – Digital compression and coding of continuous-tone still images.

Typical usage

Overall, the JPEG compression algorithm is at its best on photographs and paintings of realistic scenes with smooth variations of tone and color. Unquestionably, for web usage, where the amount of data used for an image is important, JPEG is very popular. As a result, JPEG/Exif is also the most common format for digital cameras.

On the other hand, JPEG may not be as well suited for line drawings and other textual or iconic graphics, where the sharp contrasts between adjacent pixels can cause noticeable artifacts. Such images may be better saved in a lossless graphics format such as TIFF, GIF, PNG, or a raw image format. Additionally, the JPEG standard includes a lossless coding mode too. However, most products do not support that mode.

As the typical use of JPEG is a lossy compression method, which somewhat reduces the image fidelity, it should not be used in scenarios where the exact reproduction of the data is required (such as in some scientific and medical imaging applications and certain technical image processing work).

JPEG is also not well suited to files that will undergo multiple edits, as some image quality will usually be lost each time the image is decompressed and recompressed, particularly if the image is cropped or shifted, or if encoding parameters are changed – see digital generation loss for details. To avoid this, an image that is being modified or may be modified in the future can be saved in a lossless format, with a copy exported as JPEG for distribution.

JPEG compression

JPEG uses a lossy form of compression. It is based on the discrete cosine transform (DCT). This mathematical operation converts each frame/field of the video source from the spatial (2D) domain into the frequency domain (aka transform domain.) A perceptual model based loosely on the human psycho-visual system discards high-frequency information. I.e. sharp transitions in intensity, and color hue. Quantization in the transform domain is the process of reducing information In simpler terms. Quantization is a method for optimally reducing a large number scale (with different occurrences of each number) into a smaller one. The transform domain is a convenient representation of the image because of the high-frequency coefficients. They contribute less to the over picture than other coefficients. They are characteristically small values with high compressibility.

The quantized coefficients are then sequenced and losslessly packed into the output bitstream. Nearly all software implementations of JPEG permit user control over the compression ratio (as well as other optional parameters), allowing the user to trade off picture quality for a smaller file size. In embedded applications (such as miniDV, which uses a similar DCT-compression scheme), the parameters are pre-selected and fixed for the application.

The compression method is usually lossy. To clarify, this means that some original image information is lost and cannot be restored. Undoubtedly, this affects the image quality. Of course, there is an optional lossless mode in the JPEG standard too. However, most products do not support this mode.

Progressive JPEG

There is also an interlaced “Progressive JPEG” format. It uses the compression of data in multiple passes of progressively higher detail. This is ideal in order to display large images over a slow connection. It allows a reasonable preview after receiving only a portion of the data. However, support for progressive JPEGs is not universal. In this case, programs that do not support progressive JPEGs will display the image after the download is complete. An example is versions of Internet Explorer before Windows 7.

There are also many medical imaging and traffic systems that create and process 12-bit JPEG images, normally grayscale images. That is why, the 12-bit JPEG format has been part of the JPEG specification for some time, but this format is not as widely supported.

Syntax and structure

A JPEG image consists of a sequence of segments, each beginning with a marker, each of which begins with a 0xFF byte followed by a byte indicating what kind of marker it is. Some markers consist of just those two bytes. Others are followed by two bytes indicating the length of market-specific payload data that follows. The length includes the two bytes for the length, but not the two bytes for the marker. Some markers are followed by entropy-coded data; the length of such a marker does not include the entropy-coded data.

Note that consecutive 0xFF bytes are used as fill bytes for padding purposes. Although this fill byte padding should only ever take place for markers immediately following entropy. Coded scan data (see JPEG specification section B.1.1.2 and E.1.2 for details; specifically “In all cases where markers are appended after the compressed data, optional 0xFF fill bytes may precede the marker”).

Within the entropy-coded data, after any 0xFF byte, the encoder inserts a 0x00 byte before the next byte. That is so that there does not appear to be a marker where none is intended. This prevents framing errors. Decoders must skip this 0x00 byte. This technique, called byte stuffing (see JPEG specification section F.1.2.3), is part only of the entropy-coded data, not marker payload data. Note however that entropy-coded data has a few markers of its own. Specifically, the Reset markers (0xD0 through 0xD7). They are used to isolate independent chunks of entropy-coded data to allow parallel decoding. Encoders are free to insert these Reset markers at regular intervals (although not all encoders do this).

Lossless editing

See also: jpegtran and Commons:User:Cropbot

You can perform a number of alterations to a JPEG image losslessly. That is, without recompression and the associated quality loss. You can do that as long as the image size is a multiple of 1 MCU block (Minimum Code Unit). That is usually 16 pixels in both directions, for 4:2:0 chroma subsampling. Utilities that implement this include jpegtran, with user interface Jpegcrop, and the JPG_TRANSFORM plugin to IrfanView.

You can rotate in 90-degree increments, flip in the horizontal, vertical, and diagonal axes, and move blocks in the image. In the same way, you do not need to use all blocks from the original image in the modified one.

Lossless Crop

The top and left edge of a JPEG image must lie on an 8 × 8 pixel block boundary, but the bottom and right edge need not do so. This limits the possible lossless crop operations and also prevents flips and rotations of an image whose bottom or right edge does not lie on a block boundary for all channels (because the edge would end up on top or left, where – as aforementioned – a block boundary is obligatory).

When using lossless cropping, if the bottom or right side of the crop region is not on a block boundary then the rest of the data from the partially used blocks will still be present in the cropped file and can be recovered. You can transform between baseline and progressive formats without any loss of quality. That is because the only difference is the order of the coefficients in the file.

Furthermore, you can losslessly join together several JPEG images as long as the edges coincide with block boundaries.

JPEG files

The file format known as “JPEG Interchange Format” (JIF) is specified in Annex B of the standard. However, this “pure” file format is rarely used. That is primarily because of the difficulty of programming encoders and decoders that fully implement all aspects of the standard and because of certain shortcomings of the standard:

Color space definition
Component sub-sampling registration
Pixel aspect ratio definition.

Several additional standards have evolved to address these issues. The first of these, released in 1992, was JPEG File Interchange Format (or JFIF). Then it was followed in recent years by Exchangeable image file format (Exif) and ICC color profiles. Both of these formats use the actual JIF byte layout, consisting of different markers, but in addition employ one of the JIF standard’s extension points, namely the application markers: JFIF uses APP0, while Exif uses APP1. Within these segments of the file, that were left for future use in the JIF standard and aren’t read by it. These standards add specific metadata.

Thus, in some ways, JFIF is a cutdown version of the JIF standard in that it specifies certain constraints (such as not allowing all the different encoding modes), while in other ways it is an extension of JIF due to the added metadata. The documentation for the original JFIF standard states:

Storing JPEG Files

JPEG File Interchange Format is a minimal file format. It makes it possible to exchange JPEG bitstreams between a wide variety of platforms and applications. However, this minimal format does not include any of the advanced features found in the TIFF JPEG specification or any application-specific file format. Nor should it, for the only purpose of this simplified format is to allow the exchange of JPEG compressed images.

“JPEG files” are the image files that employ JPEG compression. They use variants of the JIF image format for containers. Most image capture devices (such as digital cameras) that output JPEG is actually creating files in the Exif format, the format that the camera industry has standardized for metadata interchange. On the other hand, since the Exif standard does not allow color profiles, most image editing software stores JPEG in JFIF format, and also includes the APP1 segment from the Exif file to include the metadata in an almost-compliant way; the JFIF standard is interpreted somewhat flexibly.

Strictly speaking, the JFIF and Exif standards are incompatible because they each specify that their marker segment (APP0 or APP1, respectively) appears first. In practice, most JPEG files contain a JFIF marker segment that precedes the Exif header. This allows older readers to correctly handle the older format JFIF segment, while newer readers also decode the following Exif segment, being less strict about requiring it to appear first.

JPEG filename extensions

The most common filename extensions for files with JPEG compression are .jpg and .jpeg. Rarely they have .jpe, .jfif and .jif extensions. You can embed JPEG data in other file types too. For example, TIFF encoded files often embed a JPEG image as a thumbnail of the main image. Also, MP3 files can contain a JPEG of cover art, in the ID3v2 tag.

Color profile

Many JPEG files embed an ICC color profile (color space). Commonly used color profiles include sRGB and Adobe RGB. Because these color spaces use a non-linear transformation, the dynamic range of an 8-bit JPEG file is about 11 stops; see gamma curve.

Effects of JPEG compression

JPEG compression artifacts blend well into photographs with detailed non-uniform textures, allowing higher compression ratios. Notice how a higher compression ratio first affects the high-frequency textures in the upper-left corner of the image, and how the contrasting lines become fuzzier. The very high compression ratio severely affects the quality of the image, although the overall colors and image form are still recognizable. However, the precision of colors suffers less (for a human eye) than the precision of contours (based on luminance). This justifies the fact that images should be first transformed in a color model separating the luminance from the chromatic information, before subsampling the chromatic planes (which may also use lower quality quantization) in order to preserve the precision of the luminance plane with more information bits.

Lossless further compression

From 2004 to 2008, new research has emerged on ways to further compress the data contained in JPEG images without modifying the represented image. This has applications in scenarios where the original image is only available in JPEG format, and you need to reduce its size for archival or transmission. Standard general-purpose compression tools cannot significantly compress JPEG files.

Typically, such schemes take advantage of improvements to the naive scheme for coding DCT coefficients, which fails to take into account:

1. Correlations between magnitudes of adjacent coefficients in the same block;
2. Correlations between magnitudes of the same coefficient in adjacent blocks;
3. Correlations between magnitudes of the same coefficient/block in different channels;

The DC coefficients when taken together resemble a downscale version of the original image multiplied by a scaling factor. You can apply well-known schemes for lossless coding of continuous-tone images too. They achieve somewhat better compression than the Huffman coded DPCM used in JPEG.

JPEG Improvements

Some standard but rarely used options already exist in JPEG to improve the efficiency of coding DCT coefficients. Those are the arithmetic coding option and the progressive coding option. It will produce lower bitrates because it codes values for each coefficient independently. In addition, each coefficient has a significantly different distribution. Modern methods have improved on these techniques by reordering coefficients to group coefficients of larger magnitude together. Using adjacent coefficients and blocks to predict new coefficient values. Dividing blocks or coefficients up among a small number of independently coded models based on their statistics and adjacent values. Most recently, by decoding blocks, predicting subsequent blocks in the spatial domain, and then encoding these to generate predictions for DCT coefficients.

Typically, such methods can compress existing JPEG files between 15 and 25 percent, and for JPEGs compressed at low-quality settings, can produce improvements of up to 65%.

A freely available tool called packJPG is based on the 2007 paper “Improved Redundancy Reduction for JPEG Files.”

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