JNG Background Remover
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Background removal separates a subject from its surroundings so you can place it on transparency, swap the scene, or composite it into a new design. Under the hood you’re estimating an alpha matte—a per-pixel opacity from 0 to 1—and then alpha-compositing the foreground over something else. This is the math from Porter–Duff and the cause of familiar pitfalls like “fringes” and straight vs. premultiplied alpha. For practical guidance on premultiplication and linear color, see Microsoft’s Win2D notes, Søren Sandmann, and Lomont’s write-up on linear blending.
The main ways people remove backgrounds
1) Chroma key (“green/blue screen”)
If you can control capture, paint the backdrop a solid color (often green) and key that hue away. It’s fast, battle-tested in film and broadcast, and ideal for video. The trade-offs are lighting and wardrobe: colored light spills onto edges (especially hair), so you’ll use despill tools to neutralize contamination. Good primers include Nuke’s docs, Mixing Light, and a hands-on Fusion demo.
2) Interactive segmentation (classic CV)
For single images with messy backgrounds, interactive algorithms need a few user hints—e.g., a loose rectangle or scribbles—and converge to a crisp mask. The canonical method is GrabCut (book chapter), which learns color models for foreground/background and uses graph cuts iteratively to separate them. You’ll see similar ideas in GIMP’s Foreground Select based on SIOX (ImageJ plugin).
3) Image matting (fine-grained alpha)
Matting solves fractional transparency at wispy boundaries (hair, fur, smoke, glass). Classic closed-form matting takes a trimap (definitely-fore/definitely-back/unknown) and solves a linear system for alpha with strong edge fidelity. Modern deep image matting trains neural nets on the Adobe Composition-1K dataset (MMEditing docs), and is evaluated with metrics like SAD, MSE, Gradient, and Connectivity (benchmark explainer).
4) Deep learning cutouts (no trimap)
- U2-Net (salient-object detection) is a strong general “remove background” engine (repo).
- MODNet targets real-time portrait matting (PDF).
- F, B, Alpha (FBA) Matting jointly predicts foreground, background, and alpha to reduce color halos (repo).
- Background Matting V2 assumes a background plate and yields strand-level mattes in real time at up to 4K/30fps (project page, repo).
Related segmentation work is also useful: DeepLabv3+ refines boundaries with an encoder–decoder and atrous convolutions (PDF); Mask R-CNN gives per-instance masks (PDF); and SAM (Segment Anything) is a promptable foundation model that zero-shots masks on unfamiliar images.
What popular tools do
- Photoshop: Remove Background quick action runs “Select Subject → layer mask” under the hood (confirmed here; tutorial).
- GIMP: Foreground Select (SIOX).
- Canva: 1-click Background Remover for images and short video.
- remove.bg: web app + API for automation.
- Apple devices: system-level “Lift Subject” in Photos/Safari/Quick Look (cutouts on iOS).
Workflow tips for cleaner cutouts
- Shoot smart. Good lighting and strong subject–background contrast help every method. With green/blue screens, plan for despill (guide).
- Start broad, refine narrow. Run an automatic selection (Select Subject, U2-Net, SAM), then refine edges with brushes or matting (e.g., closed-form).
- Mind semi-transparency. Glass, veils, motion blur, flyaway hair need true alpha (not just a hard mask). Methods that also recover F/B/α minimize halos.
- Know your alpha. Straight vs. premultiplied produce different edge behavior; export/composite consistently (see overview, Hargreaves).
- Pick the right output. For “no background,” deliver a raster with a clean alpha (e.g., PNG/WebP) or keep layered files with masks if further edits are expected. The key is the quality of the alpha you computed—rooted in Porter–Duff.
Quality & evaluation
Academic work reports SAD, MSE, Gradient, and Connectivity errors on Composition-1K. If you’re picking a model, look for those metrics (metric defs; Background Matting metrics section). For portraits/video, MODNet and Background Matting V2 are strong; for general “salient object” images, U2-Net is a solid baseline; for tough transparency, FBA can be cleaner.
Common edge cases (and fixes)
- Hair & fur: favor matting (trimap or portrait matting like MODNet) and inspect on a checkerboard.
- Fine structures (bike spokes, fishing line): use high-res inputs and a boundary-aware segmenter such as DeepLabv3+ as a pre-step before matting.
- See-through stuff (smoke, glass): you need fractional alpha and often foreground color estimation (FBA).
- Video conferencing: if you can capture a clean plate, Background Matting V2 looks more natural than naive “virtual background” toggles.
Where this shows up in the real world
- E-commerce: marketplaces (e.g., Amazon) often require a pure white main image background; see Product image guide (RGB 255,255,255).
- Design tools: Canva’s Background Remover and Photoshop’s Remove Background streamline quick cutouts.
- On-device convenience: iOS/macOS “Lift Subject” is great for casual sharing.
Why cutouts sometimes look fake (and fixes)
- Color spill: green/blue light wraps onto the subject—use despill controls or targeted color replacement.
- Halo/fringes: usually an alpha-interpretation mismatch (straight vs. premultiplied) or edge pixels contaminated by the old background; convert/interpret correctly (overview, details).
- Wrong blur/grain: paste a razor-sharp subject into a soft background and it pops; match lens blur and grain after compositing (see Porter–Duff basics).
TL;DR playbook
- If you control capture: use chroma key; light evenly; plan despill.
- If it’s a one-off photo: try Photoshop’s Remove Background, Canva’s remover, or remove.bg; refine with brushes/matting for hair.
- If you need production-grade edges: use matting ( closed-form or deep) and check alpha on transparency; mind alpha interpretation.
- For portraits/video: consider MODNet or Background Matting V2; for click-guided segmentation, SAM is a powerful front-end.
What is the JNG format?
JPEG Network Graphics
The JNG (JPEG Network Graphics) format is an image file format that was designed as a sub-format of the more widely known MNG (Multiple-image Network Graphics) format. It was primarily developed to provide a solution for lossy and lossless compression within a single image format, which was not possible with other common formats such as JPEG or PNG at the time of its creation. JNG files are typically used for images that require both a high-quality, photographic-style representation and an optional alpha channel for transparency, which is not supported by standard JPEG images.
JNG is not a standalone format but is part of the MNG file format suite, which was designed to be the animated version of PNG. The MNG suite includes both MNG and JNG formats, with MNG supporting animations and JNG being a single-image format. The JNG format was created by the same team that developed the PNG format, and it was intended to complement PNG by adding JPEG-compressed color data while maintaining the possibility of a separate alpha channel, which is a feature that PNG supports but JPEG does not.
The structure of a JNG file is similar to that of a MNG file, but it is simpler since it is intended for single images only. A JNG file consists of a series of chunks, each of which contains a specific type of data. The most important chunks in a JNG file are the JHDR chunk, which contains the header information; the JDAT chunk, which contains the JPEG-compressed image data; the JSEP chunk, which may be present to indicate the end of the JPEG data stream; and the alpha channel chunks, which are optional and can be either IDAT chunks (containing PNG-compressed alpha data) or JDAA chunks (containing JPEG-compressed alpha data).
The JHDR chunk is the first chunk in a JNG file and is critical as it defines the properties of the image. It includes information such as the image's width and height, color depth, whether an alpha channel is present, the color space used, and the compression method for the alpha channel. This chunk allows decoders to understand how to process the subsequent data within the file.
The JDAT chunk contains the actual image data, which is compressed using the JPEG standard compression techniques. This compression allows for efficient storage of photographic images, which often contain complex color gradients and subtle variations in tone. The JPEG compression within JNG is identical to that used in standalone JPEG files, making it possible for standard JPEG decoders to read the image data from a JNG file without needing to understand the entire JNG format.
If an alpha channel is present in the JNG image, it is stored in either IDAT or JDAA chunks. The IDAT chunks are the same as those used in PNG files and contain PNG-compressed alpha data. This allows for lossless compression of the alpha channel, ensuring that transparency information is preserved without any quality loss. The JDAA chunks, on the other hand, contain JPEG-compressed alpha data, which allows for smaller file sizes at the cost of potential lossy compression artifacts in the alpha channel.
The JSEP chunk is an optional chunk that signals the end of the JPEG data stream. It is useful in cases where the JNG file is being streamed over a network, and the decoder needs to know when to stop reading JPEG data and start looking for alpha channel data. This chunk is not required if the file is being read from a local storage medium where the end of the JPEG data can be determined from the file structure itself.
JNG also supports color correction by including an ICCP chunk, which contains an embedded ICC color profile. This profile allows for accurate color representation across different devices and is particularly important for images that will be viewed on a variety of screens or printed. The inclusion of color management capabilities is a significant advantage of the JNG format over standalone JPEG files, which do not inherently support embedded color profiles.
Despite its capabilities, the JNG format has not seen widespread adoption. This is partly due to the dominance of the JPEG format for photographic images and the PNG format for images requiring transparency. Additionally, the rise of formats like WebP and HEIF, which also support both lossy and lossless compression as well as transparency, has further reduced the need for a separate format like JNG. However, JNG remains a viable option for specific use cases where its unique combination of features is required.
One of the reasons for the lack of widespread adoption of JNG is the complexity of the MNG file format suite. While JNG itself is relatively simple, it is part of a larger and more complex set of specifications that were not widely implemented. Many software developers chose to support the simpler and more popular JPEG and PNG formats instead, which met most users' needs without the additional complexity of MNG and JNG.
Another factor that has limited the adoption of JNG is the lack of support in popular image editing and viewing software. While some specialized software may support JNG, many of the most commonly used programs do not. This lack of support means that users and developers are less likely to encounter or use JNG files, further diminishing its presence in the marketplace.
Despite these challenges, JNG does have its proponents, particularly among those who appreciate its technical capabilities. For instance, JNG can be useful in applications where a single file needs to contain both a high-quality photographic image and a separate alpha channel for transparency. This can be important in graphic design, game development, and other fields where images need to be composited against various backgrounds.
The technical design of JNG also allows for potential optimizations in file size and quality. For example, by separating the color and alpha data, it is possible to apply different levels of compression to each, optimizing for the best balance between file size and image quality. This can result in smaller files than if a single compression method were applied to the entire image, as is the case with formats like PNG.
In conclusion, the JNG image format is a specialized file format that offers a unique combination of features, including support for both lossy and lossless compression, an optional alpha channel for transparency, and color management capabilities. While it has not achieved widespread adoption, it remains a technically capable format that may be suitable for specific applications. Its future relevance will likely depend on whether there is a renewed interest in its capabilities and whether software support for the format expands. For now, JNG stands as a testament to the ongoing evolution of image formats and the search for the perfect balance of compression, quality, and functionality.
Supported formats
AAI.aai
AAI Dune image
AI.ai
Adobe Illustrator CS2
AVIF.avif
AV1 Image File Format
BAYER.bayer
Raw Bayer Image
BMP.bmp
Microsoft Windows bitmap image
CIN.cin
Cineon Image File
CLIP.clip
Image Clip Mask
CMYK.cmyk
Raw cyan, magenta, yellow, and black samples
CUR.cur
Microsoft icon
DCX.dcx
ZSoft IBM PC multi-page Paintbrush
DDS.dds
Microsoft DirectDraw Surface
DPX.dpx
SMTPE 268M-2003 (DPX 2.0) image
DXT1.dxt1
Microsoft DirectDraw Surface
EPDF.epdf
Encapsulated Portable Document Format
EPI.epi
Adobe Encapsulated PostScript Interchange format
EPS.eps
Adobe Encapsulated PostScript
EPSF.epsf
Adobe Encapsulated PostScript
EPSI.epsi
Adobe Encapsulated PostScript Interchange format
EPT.ept
Encapsulated PostScript with TIFF preview
EPT2.ept2
Encapsulated PostScript Level II with TIFF preview
EXR.exr
High dynamic-range (HDR) image
FF.ff
Farbfeld
FITS.fits
Flexible Image Transport System
GIF.gif
CompuServe graphics interchange format
HDR.hdr
High Dynamic Range image
HEIC.heic
High Efficiency Image Container
HRZ.hrz
Slow Scan TeleVision
ICO.ico
Microsoft icon
ICON.icon
Microsoft icon
J2C.j2c
JPEG-2000 codestream
J2K.j2k
JPEG-2000 codestream
JNG.jng
JPEG Network Graphics
JP2.jp2
JPEG-2000 File Format Syntax
JPE.jpe
Joint Photographic Experts Group JFIF format
JPEG.jpeg
Joint Photographic Experts Group JFIF format
JPG.jpg
Joint Photographic Experts Group JFIF format
JPM.jpm
JPEG-2000 File Format Syntax
JPS.jps
Joint Photographic Experts Group JPS format
JPT.jpt
JPEG-2000 File Format Syntax
JXL.jxl
JPEG XL image
MAP.map
Multi-resolution Seamless Image Database (MrSID)
MAT.mat
MATLAB level 5 image format
PAL.pal
Palm pixmap
PALM.palm
Palm pixmap
PAM.pam
Common 2-dimensional bitmap format
PBM.pbm
Portable bitmap format (black and white)
PCD.pcd
Photo CD
PCT.pct
Apple Macintosh QuickDraw/PICT
PCX.pcx
ZSoft IBM PC Paintbrush
PDB.pdb
Palm Database ImageViewer Format
PDF.pdf
Portable Document Format
PDFA.pdfa
Portable Document Archive Format
PFM.pfm
Portable float format
PGM.pgm
Portable graymap format (gray scale)
PGX.pgx
JPEG 2000 uncompressed format
PICT.pict
Apple Macintosh QuickDraw/PICT
PJPEG.pjpeg
Joint Photographic Experts Group JFIF format
PNG.png
Portable Network Graphics
PNG00.png00
PNG inheriting bit-depth, color-type from original image
PNG24.png24
Opaque or binary transparent 24-bit RGB (zlib 1.2.11)
PNG32.png32
Opaque or binary transparent 32-bit RGBA
PNG48.png48
Opaque or binary transparent 48-bit RGB
PNG64.png64
Opaque or binary transparent 64-bit RGBA
PNG8.png8
Opaque or binary transparent 8-bit indexed
PNM.pnm
Portable anymap
PPM.ppm
Portable pixmap format (color)
PS.ps
Adobe PostScript file
PSB.psb
Adobe Large Document Format
PSD.psd
Adobe Photoshop bitmap
RGB.rgb
Raw red, green, and blue samples
RGBA.rgba
Raw red, green, blue, and alpha samples
RGBO.rgbo
Raw red, green, blue, and opacity samples
SIX.six
DEC SIXEL Graphics Format
SUN.sun
Sun Rasterfile
SVG.svg
Scalable Vector Graphics
TIFF.tiff
Tagged Image File Format
VDA.vda
Truevision Targa image
VIPS.vips
VIPS image
WBMP.wbmp
Wireless Bitmap (level 0) image
WEBP.webp
WebP Image Format
YUV.yuv
CCIR 601 4:1:1 or 4:2:2
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