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.
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.
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).
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).
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.
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.
The JPS image format, short for JPEG Stereo, is a file format used to store stereoscopic photographs taken by digital cameras or created by 3D rendering software. It is essentially a side-by-side arrangement of two JPEG images within a single file that, when viewed through appropriate software or hardware, provides a 3D effect. This format is particularly useful for creating an illusion of depth in images, which enhances the viewing experience for users with compatible display systems or 3D glasses.
The JPS format leverages the well-established JPEG (Joint Photographic Experts Group) compression technique to store the two images. JPEG is a lossy compression method, which means that it reduces file size by selectively discarding less important information, often without a noticeable decrease in image quality to the human eye. This makes JPS files relatively small and manageable, despite containing two images instead of one.
A JPS file is essentially a JPEG file with a specific structure. It contains two JPEG-compressed images side by side within a single frame. These images are called the left-eye and right-eye images, and they represent slightly different perspectives of the same scene, mimicking the slight difference between what each of our eyes sees. This difference is what allows for the perception of depth when the images are viewed correctly.
The standard resolution for a JPS image is typically twice the width of a standard JPEG image to accommodate both the left and right images. For example, if a standard JPEG image has a resolution of 1920x1080 pixels, a JPS image would have a resolution of 3840x1080 pixels, with each side-by-side image occupying half of the total width. However, the resolution can vary depending on the source of the image and the intended use.
To view a JPS image in 3D, a viewer must use a compatible display device or software that can interpret the side-by-side images and present them to each eye separately. This can be achieved through various methods, such as anaglyph 3D, where the images are filtered by color and viewed with colored glasses; polarized 3D, where the images are projected through polarized filters and viewed with polarized glasses; or active shutter 3D, where the images are displayed alternately and synchronized with shutter glasses that open and close rapidly to show each eye the correct image.
The file structure of a JPS image is similar to that of a standard JPEG file. It contains a header, which includes the SOI (Start of Image) marker, followed by a series of segments that contain various pieces of metadata and the image data itself. The segments include the APP (Application) markers, which can contain information such as the Exif metadata, and the DQT (Define Quantization Table) segment, which defines the quantization tables used for compressing the image data.
One of the key segments in a JPS file is the JFIF (JPEG File Interchange Format) segment, which specifies that the file conforms to the JFIF standard. This segment is important for ensuring compatibility with a wide range of software and hardware. It also includes information such as the aspect ratio and resolution of the thumbnail image, which can be used for quick previews.
The actual image data in a JPS file is stored in the SOS (Start of Scan) segment, which follows the header and metadata segments. This segment contains the compressed image data for both the left and right images. The data is encoded using the JPEG compression algorithm, which involves a series of steps including color space conversion, subsampling, discrete cosine transform (DCT), quantization, and entropy coding.
Color space conversion is the process of converting the image data from the RGB color space, which is commonly used in digital cameras and computer displays, to the YCbCr color space, which is used in JPEG compression. This conversion separates the image into a luminance component (Y), which represents the brightness levels, and two chrominance components (Cb and Cr), which represent the color information. This is beneficial for compression because the human eye is more sensitive to changes in brightness than color, allowing for more aggressive compression of the chrominance components without significantly affecting perceived image quality.
Subsampling is a process that takes advantage of the human eye's lower sensitivity to color detail by reducing the resolution of the chrominance components relative to the luminance component. Common subsampling ratios include 4:4:4 (no subsampling), 4:2:2 (reducing the horizontal resolution of the chrominance by half), and 4:2:0 (reducing both the horizontal and vertical resolution of the chrominance by half). The choice of subsampling ratio can affect the balance between image quality and file size.
The discrete cosine transform (DCT) is applied to small blocks of the image (typically 8x8 pixels) to convert the spatial domain data into the frequency domain. This step is crucial for JPEG compression because it allows for the separation of image details into components of varying importance, with higher frequency components often being less perceptible to the human eye. These components can then be quantized, or reduced in precision, to achieve compression.
Quantization is the process of mapping a range of values to a single quantum value, effectively reducing the precision of the DCT coefficients. This is where the lossy nature of JPEG compression comes into play, as some image information is discarded. The degree of quantization is determined by the quantization tables specified in the DQT segment, and it can be adjusted to balance image quality against file size.
The final step in the JPEG compression process is entropy coding, which is a form of lossless compression. The most common method used in JPEG is Huffman coding, which assigns shorter codes to more frequent values and longer codes to less frequent values. This reduces the overall size of the image data without any further loss of information.
In addition to the standard JPEG compression techniques, the JPS format may also include specific metadata that relates to the stereoscopic nature of the images. This metadata can include information about the parallax settings, convergence points, and any other data that may be necessary for correctly displaying the 3D effect. This metadata is typically stored in the APP segments of the file.
The JPS format is supported by a variety of software applications and devices, including 3D televisions, VR headsets, and specialized photo viewers. However, it is not as widely supported as the standard JPEG format, so users may need to use specific software or convert the JPS files to another format for broader compatibility.
One of the challenges with the JPS format is ensuring that the left and right images are properly aligned and have the correct parallax. Misalignment or incorrect parallax can lead to an uncomfortable viewing experience and may cause eye strain or headaches. Therefore, it is important for photographers and 3D artists to carefully capture or create the images with the correct stereoscopic parameters.
In conclusion, the JPS image format is a specialized file format designed for storing and displaying stereoscopic images. It builds upon the established JPEG compression techniques to create a compact and efficient way to store 3D photographs. While it offers a unique viewing experience, the format requires compatible hardware or software to view the images in 3D, and it may present challenges in terms of alignment and parallax. Despite these challenges, the JPS format remains a valuable tool for photographers, 3D artists, and enthusiasts who wish to capture and share the depth and realism of the world in a digital format.
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