How to Find Similar & Duplicate JPG Images in Windows 11, 10, 8

Find Similar JPG


Explore the best ways to find and remove duplicate or similar JPG files in Windows. Compare JPG with all popular image formats and get rid of repeating photos regardless of the image format.

  1. Dedicated Tools For Finding Duplicate JPG and Other Popular Image Formats
  2. How to Find Similar JPG Images (JPEG)
  3. Find Similar Images in Any Image Format
  4. Unveiling the Advantages of Eliminating Duplicate and Similar JPG Files
  5. Comprehensive JPEG File Format Information

1. Dedicated Tools For Finding Duplicate JPG and Other Popular Image Formats

Common duplicate finders lack advanced visual understanding capabilities. Identifying similarity in images requires more than just comparing file attributes; it involves understanding the visual content, shapes, and structures within images.

Finding similar JPGs or any other images is not something that a common duplicate finder application can do. VSDIF performs true image analysis to identify similarities between photos stored in different image formats. 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, and 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.

Find Similar JPG

Find Similar JPG

Because 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.

Find similar JPG files of any type and also compare them with any other image format. Visual Similarity Duplicate Image Finder supports 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)


Compatible with Windows 11/10/8.1/8/7/Vista/XP (Both 32 & 64 Bit)

2. How to Find Similar JPG Images (JPEG)

Steps to find duplicate JPG files:

  1. Add Folders: Include folders containing JPG files in the tool’s folder list.
  2. Set Similarity Percentage: Adjust the similarity percentage to determine file comparison.
  3. Start Scan: Initiate the search for repeating JPG by clicking “Start scan.”
  4. Review and Select: Examine results and choose duplicate JPG files for removal.
  5. Execute Action: Tick selected files and choose to Delete, Move, or Copy them

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.

3. 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.

Here are all the duplicate images that you can search for using this tool:

  • JPEG Bitmap (*.jpg;*.jpeg;*.jpe;*.jfif;*.jif)
  • Compuserve Bitmap (*.gif)
  • Portable Network Graphics (*.png)
  • TIFF Bitmap (*.tif;*.tiff;*.fax;*.g3n;*.g3f;*.xif)
  • JPEG2000 Files (*.jp2)
  • JPEG2000 Code Stream (*.j2k;*.jpc;*.j2c)
  • Targa (*.tga;*.targa;*.vda;*.icb;*.vst;*.pix)
  • Paintbrush (*.pcx)
  • Windows Bitmap (*.bmp;*.dib;*.rle)
  • Windows Metafile (*.wmf)
  • Enhanced Windows Metafile (*.emf)
  • Windows Icon (*.ico)
  • Windows Cursor (*.cur)
  • Wireless Bitmap (*.wbmp)
  • Portable Pixmap (*.pxm;*.ppm)
  • Portable Bitmap / Graymap (*.pgm; *.pbm)
  • Adobe Photoshop (*.psd)
  • Camera RAW (*.crw; *.cr2; *.cr3; *.fff; *.eip; *.dcs; *.drf; *.ptx; *.pxn; *.mdc; *.obm; *.nef; *.raw; *.pef; *.raf; *.x3f; *.bay; *.orf; *.srf; *.mrw; *.dcr; *.sr2; *.dng; *.erf; *.mef; *.arw) [ List of all 300+ Camera RAW formats ]
  • DICOM Images (*.dcm; *.dicom; *.dic; *.v2 )
  • HDPhoto Images (*.hdp; *.wdp; *.jxr)
  • WebP Images (*.webp)
  • HEIC Images (*.heic) (requires external WIC codec)


Compatible with Windows 11/10/8.1/8/7/Vista (Both 32 & 64 Bit)


4. Unveiling the Advantages of Eliminating Duplicate and Similar JPG Files

In the vast digital landscape, managing a growing collection of images can quickly become an overwhelming task. As digital photography and graphic design projects flourish, the proliferation of similar and duplicate JPG files adds to the complexity. While it may seem like a daunting challenge, the benefits of removing these redundant files extend far beyond just decluttering your storage. Let’s delve into the details and uncover the hidden advantages of effectively managing and eliminating duplicate and similar JPG files using a Duplicate Image Finder.

  1. Reclaiming Storage Space:

    One of the most apparent benefits of removing duplicate JPG files is the reclamation of valuable storage space. Over time, as digital libraries expand, redundant images can accumulate and consume a significant portion of disk space. Efficiently identifying and deleting these duplicates can free up room for new files and enhance overall system performance. You can free up extra space by using the free Folder Size app.

  2. Streamlining Organization:

    A cluttered image library can hinder your ability to find specific files promptly. By eliminating duplicates and similar JPG files, you streamline the organization of your digital assets. A well-organized image repository facilitates quicker access to essential files, improving workflow efficiency for both personal and professional projects.

  3. Enhancing Backup Processes:

    Maintaining backups of large image collections is a common practice to prevent data loss. However, redundant files can unnecessarily inflate the size of backups, leading to increased storage requirements and longer backup times. Removing duplicate JPG files ensures that your backup processes are more streamlined, allowing for faster and more efficient data protection.

  4. Improving System Performance:

    A system burdened with duplicate files may experience decreased performance. This is especially relevant when working with image-intensive applications or browsing through extensive image libraries. By eliminating redundancy, you reduce the strain on system resources, resulting in improved overall performance and responsiveness.

  5. Safeguarding Data Integrity:

    Duplicate JPG files can inadvertently lead to confusion, potentially causing users to work with outdated or incorrect versions of images. This can have serious implications in professional settings where accuracy is crucial. Removing duplicates safeguards data integrity by ensuring that only the most current and relevant files are retained.

  6. Optimizing Search and Retrieval:

    Searching for specific images becomes more efficient when your digital library is free from duplicates. Image search and retrieval processes are optimized, allowing you to locate files with ease. This is particularly beneficial for photographers, designers, and content creators who rely on quick access to their visual assets.

  7. Facilitating Collaboration:

    In collaborative environments where multiple users contribute to and access a shared image repository, managing duplicates becomes paramount. A unified and streamlined image database promotes collaboration by reducing the risk of users working on different versions of the same file. This, in turn, enhances team productivity and ensures consistency in visual assets.


In conclusion, the advantages of removing duplicate and similar JPG files extend beyond mere tidiness. Embracing efficient file management practices not only enhances storage capacity but also contributes to improved organization, system performance, and data integrity. As digital content continues to proliferate, adopting proactive measures to curate and optimize your image library becomes an essential aspect of digital asset management.

5. Comprehensive JPEG File Format Information

JPEG, short for Joint Photographic Experts Group, is a widely used method of lossy compression for digital images, often identified with the .jpg extension. This compression technique is particularly prevalent in images generated by digital photography. One of its notable features is the ability to adjust the degree of compression, allowing users to make a tradeoff between storage size and image quality. In practice, JPEG achieves approximately 10:1 compression with minimal perceptible loss in image quality.

Numerous image file formats employ JPEG compression, with JPEG/Exif being the most common. This format is extensively used by digital cameras and other devices for capturing photographic images. Alongside JPEG/JFIF, it stands out as the primary format for storing and transmitting photographic images on the World Wide Web. Despite variations in format, many people refer to both JPEG/Exif and JPEG/JFIF simply as JPEG.

The acronym “JPEG” originates from the Joint Photographic Experts Group, the entity that established this compression standard. The MIME media type associated with JPEG is image/jpeg, as defined by RFC 1341. Notably, Internet Explorer differs by providing a MIME type of image/pjpeg when uploading JPEG images.

JPEG/JFIF supports a maximum image size of 65535×65535, accommodating a wide range of resolutions for various applications.

The JPEG standard

The term “JPEG” is derived from the Joint Photographic Experts Group, the committee responsible for creating the JPEG standard and other coding standards for still pictures. The “Joint” originally referred to ISO TC97 WG8 and CCITT SGVIII. In 1987, ISO TC 97 became ISO/IEC JTC1, and in 1992, CCITT transformed into ITU-T. Currently, within 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, JPEG is one of two sub-groups. On the ITU-T side, ITU-T SG16 is the corresponding body. The original JPEG group was formed in 1986, and the first JPEG standard was issued in 1992, approved as ITU-T Recommendation T.81 in September 1992 and as ISO/IEC 10918-1 in 1994.

The JPEG standard specifically outlines the codec, detailing how an image is compressed into a byte stream and subsequently decompressed into an image. However, it does not define the file format used to contain this stream. The commonly used file formats for the interchange of JPEG-compressed images are defined by the Exif and JFIF standards.

Formally titled “Information technology – Digital compression and coding of continuous-tone still images,” JPEG standards play a crucial role in the digital compression and coding of continuous-tone still images in the field of information technology.

Typical usage

The JPEG compression algorithm excels when applied to photographs and paintings portraying realistic scenes with smooth tonal and color variations. It is particularly popular for web usage due to its ability to minimize data usage for images. Consequently, JPEG/Exif has become the predominant format for digital cameras.

However, JPEG may not be as well-suited for line drawings, textual, or iconic graphics, where sharp contrasts between adjacent pixels may result in noticeable artifacts. In such cases, saving images in a lossless graphics format like TIFF, GIF, PNG, or a raw image format is preferable. Although the JPEG standard does include a lossless coding mode, it is not widely supported by most products.

Given that JPEG is primarily a lossy compression method, reducing image fidelity to some extent, it is not recommended for scenarios requiring exact data reproduction, such as certain scientific and medical imaging applications or specific technical image processing work.

Furthermore, JPEG is less suitable for files undergoing multiple edits, as each decompression and recompression iteration may lead to some loss in image quality, especially with cropping, shifting, or changing encoding parameters. To mitigate this, images subject to modifications can be saved in a lossless format, with a separate copy exported as JPEG for distribution purposes.

JPEG compression

JPEG employs a lossy form of compression, relying on the discrete cosine transform (DCT). This mathematical operation converts the video source’s frames/fields from the spatial (2D) domain to the frequency domain (transform domain). A perceptual model, loosely based on the human psycho-visual system, discards high-frequency information, such as sharp transitions in intensity and color hue. Quantization in the transform domain optimally reduces a large number scale into a smaller one. The transform domain is favored due to the compressibility of its high-frequency coefficients, which contribute less to the overall picture.

The quantized coefficients are then organized and losslessly packed into the output bitstream. Most JPEG software implementations allow users to control the compression ratio and other parameters, enabling a trade-off between picture quality and file size. In embedded applications like miniDV, parameters are pre-selected and fixed. Although the compression method is generally lossy, with some original image information irreversibly lost, optional lossless modes exist in the JPEG standard, though support for this mode is limited in most products.

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 is composed of segments, each marked by a 0xFF byte, followed by a byte indicating the type of marker. Some markers consist only of these two bytes, while others are succeeded by two bytes denoting the length of marker-specific payload data. The length includes the two bytes for the length but excludes the two bytes for the marker. Entropy-coded data may follow certain markers, and the length of such a marker does not encompass the entropy-coded data.

Consecutive 0xFF bytes are employed as fill bytes for padding, occurring primarily for markers immediately following entropy-coded scan data. In entropy-coded data, after any 0xFF byte, an encoder inserts a 0x00 byte to avoid misinterpretation as a marker. This byte stuffing technique, detailed in the JPEG specification section F.1.2.3, aims to prevent framing errors, and decoders must skip this 0x00 byte. Notably, Reset markers (0xD0 through 0xD7) within the entropy-coded data isolate independent chunks for parallel decoding, and encoders may insert them at regular intervals.

Lossless editing

JPEG images can undergo various alterations without loss of quality, provided the image size aligns with a multiple of 1 MCU block (Minimum Code Unit), typically 16 pixels in both directions for 4:2:0 chroma subsampling. Tools like jpegtran, the user interface Jpegcrop, and the JPG_TRANSFORM plugin to IrfanView facilitate these lossless transformations.

It is possible to rotate the image in 90-degree increments, flip it along the horizontal, vertical, and diagonal axes, and rearrange blocks within the image. Moreover, not all blocks from the original image need to be used in the modified version, offering flexibility in preserving image integrity during adjustments.

Lossless Crop

JPEG images have specific constraints regarding their alignment to pixel block boundaries. While the top and left edges must adhere to an 8 × 8 pixel block boundary, the bottom and right edges are not bound by this requirement. This limitation affects lossless crop operations and restricts certain transformations like flips and rotations if the image’s bottom or right edge does not align with a block boundary for all color channels.

In lossless cropping, if the crop region’s bottom or right side does not align with a block boundary, the remaining data from partially used blocks will still be present in the cropped file and can be recovered. Additionally, it’s possible to seamlessly transform between baseline and progressive formats without any loss of quality, as the only difference lies in the order of coefficients in the file.

Moreover, multiple JPEG images can be joined together in a lossless manner, provided their edges coincide with block boundaries. This flexibility allows for various operations while maintaining image integrity.

JPEG files

The file format recognized as “JPEG Interchange Format” (JIF) is outlined in Annex B of the JPEG standard, yet this pure format is rarely employed. The limited usage is primarily attributed to the complexity of programming encoders and decoders that fully implement all aspects of the standard, coupled with certain shortcomings like color space definition, component sub-sampling registration, and pixel aspect ratio definition.

To address these issues, several additional standards have emerged over the years. The first, introduced in 1992, is the JPEG File Interchange Format (JFIF). More recently, Exchangeable image file format (Exif) and ICC color profiles have been established. While these formats adopt the JIF byte layout with different markers, they leverage one of the JIF standard’s extension points, specifically the application markers—JFIF uses APP0, and Exif uses APP1. These segments, initially reserved for future use in the JIF standard and not read by it, now serve to add specific metadata.

In essence, JFIF acts as both a streamlined version and an extension of the JIF standard. It specifies certain constraints, such as disallowing all encoding modes, while incorporating additional metadata. The documentation for the original JFIF standard clarifies this dual role.

Storing JPEG Files

The JPEG File Interchange Format (JFIF) is designed as a minimalist file format to simplify the exchange of JPEG bitstreams across various platforms and applications. This simplified format intentionally omits advanced features present in the TIFF JPEG specification or any application-specific file format. Its primary purpose is to facilitate the exchange of JPEG-compressed images.

In essence, “JPEG files” are image files utilizing JPEG compression and employing variants of the JIF image format for containers. Digital cameras and similar image capture devices typically output files in the Exif format, which is standardized for metadata interchange. However, the Exif standard lacks support for color profiles. Consequently, image editing software often stores JPEG images in the JFIF format. In doing so, it may include the APP1 segment from the Exif file to incorporate metadata in an almost-compliant manner, given the somewhat flexible interpretation of the JFIF standard.

From a strict standpoint, the JFIF and Exif standards are incompatible, as each specifies that its marker segment (APP0 or APP1, respectively) should appear first. However, in practice, many JPEG files feature a JFIF marker segment preceding the Exif header. This allows older readers to correctly handle the older-format JFIF segment, while newer readers decode the subsequent Exif segment, showcasing flexibility in marker segment order.

JPEG filename extensions

Files compressed with JPEG often have the filename extensions .jpg and .jpeg, while less common extensions include .jpe, .jfif, and .jif. Additionally, JPEG data can be embedded in other file types. For instance, TIFF-encoded files may include a JPEG image as a thumbnail for the main image. Moreover, MP3 files can incorporate a JPEG image for cover art within the ID3v2 tag. This versatility allows JPEG compression to be utilized in various contexts and file formats.

Color profile

Numerous JPEG files include an ICC color profile, which defines the color space used in the image. Commonly employed color profiles include sRGB and Adobe RGB. These color spaces utilize a non-linear transformation, impacting the dynamic range of an 8-bit JPEG file, which is approximately 11 stops. The gamma curve plays a crucial role in shaping the tonal representation within JPEG images.

Effects of JPEG compression

JPEG compression artifacts integrate smoothly into photographs that feature detailed, non-uniform textures, allowing for higher compression ratios. Observe how a heightened compression ratio initially impacts the high-frequency textures in the upper-left corner of the image, causing the contrasting lines to become fuzzier. While a very high compression ratio significantly affects image quality, the overall colors and form remain recognizable.

Notably, the precision of colors is less affected (to the human eye) than the precision of contours based on luminance. This underscores the importance of transforming images into a color model that separates luminance from chromatic information before subsampling the chromatic planes, preserving the precision of the luminance plane with more information bits.

Lossless further compression

Between 2004 and 2008, new research emerged to explore ways to further compress the data within JPEG images without modifying the represented image. This becomes particularly relevant in scenarios where the original image is only available in JPEG format, and there is a need to reduce its size for archival or transmission purposes. Standard general-purpose compression tools are not effective at significantly compressing JPEG files.

These schemes typically capitalize on improvements to the naive scheme for coding Discrete Cosine Transform (DCT) coefficients, taking 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 considered together, resemble a downscaled version of the original image multiplied by a scaling factor. Well-known schemes for lossless coding of continuous-tone images can be applied, achieving somewhat better compression than the Huffman-coded Differential Pulse Code Modulation (DPCM) used in JPEG.

JPEG Improvements

JPEG offers standard but rarely used options to enhance the efficiency of coding Discrete Cosine Transform (DCT) coefficients. These include the arithmetic coding option and the progressive coding option. These options aim to produce lower bitrates by coding values for each coefficient independently, considering their significantly different distributions. However, modern methods have evolved to improve these techniques:

  1. Reordering Coefficients: Group coefficients of larger magnitude together, enhancing compression efficiency.
  2. Prediction Techniques: Use adjacent coefficients and blocks to predict new coefficient values, leveraging statistical correlations.
  3. Independent Coding Models: Divide blocks or coefficients among a small number of independently coded models based on their statistics and adjacent values.
  4. Spatial Domain Prediction: Decode blocks, predict subsequent blocks in the spatial domain and then encode these to generate predictions for DCT coefficients.

These advanced methods typically achieve compression improvements of 15 to 25 percent for existing JPEG files. In cases of low-quality settings, enhancements of up to 65% are possible. An example tool, packJPG, is freely available and is based on the principles outlined in the 2007 paper “Improved Redundancy Reduction for JPEG Files.”


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