Systems and methods for digital image compression

Abstract

Described herein are examples of imaging systems for digital image processing. For example, techniques are disclosed for dynamic compression of individual regions representing pixels and / or voxels of a high-resolution digital image. During image data compression, a first region can be scaled based on a first scaling value, while a second region can be scaled based on a second scaling value. During image data decompression and image reconstruction, a third scaling value is applied to both the first and second regions.

Description

[Technical Field]

[0001] [CROSS-REFERENCE TO RELATED APPLICATIONS] This application is a non-provisional application of U.S. Provisional Patent Application No. 63 / 406,827, filed September 15, 2022, entitled "Systems And Methods For Digital Image Compression," the contents of which are incorporated herein by reference in their entirety.

[0002] This disclosure relates generally to digital image processing, and more particularly to systems and methods for digital image compression and / or decompression processes. [Background technology]

[0003] Industrial radiography imaging systems are used to acquire two-dimensional (2D) and / or three-dimensional (3D) radiographic images of parts used in industrial applications. Such industrial applications may include, for example, aerospace, automotive, electronic, medical, pharmaceutical, military, and / or defense applications. The radiographic images may be stored for subsequent access and / or manipulation. However, the images may have large file sizes and may not be easily reconstructed.

[0004] Therefore, systems and methods for improving image storage and reconstruction are desirable.

[0005] By comparing such systems and methods with the present disclosure, which is described in the remainder of this application with reference to the drawings, the limitations and disadvantages of the conventional and traditional approaches will become apparent to one skilled in the art. Summary of the Invention

[0006] The present disclosure is directed to a high-resolution imaging process including compression, storage, and / or reconstruction of digital images as generally illustrated by and / or described with respect to at least one of the figures, and as more fully defined by the claims.

[0007] These and other advantages, aspects, and novel features of the present disclosure, as well as details of illustrated examples of the present disclosure, will be more fully understood from the following description and drawings. [Brief explanation of the drawings]

[0008] [Figure 1] FIG. 1 illustrates an example of an imaging device according to aspects of the present disclosure.

[0009] [Figure 2] 2 is a block diagram illustrating an example system using the imaging equipment of FIG. 1 according to an aspect of the present disclosure.

[0010] [Figure 3A-3B] 3 illustrates an example image represented by processed image data from the imaging equipment of FIGS. 1 and 2, according to aspects of the present disclosure.

[0011] [Figure 4A-4B] 3 illustrates another example image represented by processed image data from the imaging device of FIGS. 1 and 2, according to aspects of the present disclosure.

[0012] [Figure 5A-5B] 3 is a flowchart illustrating an example operation of an imaging process of the imaging system of FIGS. 1 and 2, according to an aspect of the present disclosure. DETAILED DESCRIPTION OF THE INVENTION

[0013] The figures are not necessarily to scale. Where appropriate, the same or similar reference numbers are used in the figures to refer to similar or identical components.

[0014] The present disclosure is directed to systems and methods for digital image processing. For example, techniques are disclosed for compressing individual regions representing pixels and / or voxels of high-resolution digital images. In some examples, the images are acquired through an industrial radiography imaging process.

[0015] Image compression techniques are designed to compress and reconstruct digital images to facilitate the storage and / or transmission of large digital files. This can include a variety of digital image types and formats, including 2D and 3D images constructed from pixels and voxels, respectively. In conventional compression systems, the raw volumetric data corresponding to an image is scaled to smaller units for storage or transmission, such as scaling each voxel of the image as a 32-bit floating-point voxel (e.g., single-precision floating-point format) to an 8-bit or 16-bit volume, thereby reducing the file size.

[0016] In conventional systems, the above scaling and export techniques (e.g., 32-bit to 8-bit or 32-bit to 16-bit) result in undesirable quantization effects. For example, if the image volume has a wide range of densities (e.g., voxel gray values), scaling can result in not only a loss of data / image quality, but also slower image reconstruction.

[0017] The disclosed systems and methods provide advanced techniques by scaling image data from an initial value, initial depth, initial volume, or initial size (e.g., 32 bits) to one or more secondary values, secondary depths, secondary volumes, or secondary sizes (e.g., 8 bits or 16 bits) based on one or more characteristics (i.e., minimum and / or maximum volume values). In particular, in the case of three-dimensional images, the total volume of the image can be defined as including multiple individual regions having the total volume. In some examples, one or more of these regions correspond to voxels (e.g., a 32-bit by 32-bit by 32-bit voxel "brick") having a defined value, depth, size, or volume. In some examples, each region or each brick has a common value, while in other examples, two regions or two bricks can be defined by different values.

[0018] During compression of image data (e.g., during compression for storage and / or transmission), each region initial value (e.g., a range of values ​​that may include 32-bit minimum / maximum volume values) is scaled to one or more secondary values ​​(e.g., a range of values, such as 8-bit or 16-bit minimum / maximum volume values). In particular, a first region of the plurality of regions may be scaled from a 32-bit volume to an 8-bit volume, while a second region of the plurality of regions may be scaled from a 32-bit volume to a 16-bit volume. Identification of a region and assignment of one of various compression scales (e.g., over a range of values, such as 8-bit or 16-bit values) may be performed based on one or more characteristics of the image, such as gray-value range, gray-value distribution (e.g., quantization), noise in the image, contrast, density, location within the image, or other suitable characteristics. For example, the characteristics may correspond to a value and / or range of values ​​for a range of gray levels at a particular pixel or voxel. As such, the compression or scaling value may be any suitable number, for example, within a range of 0-bit values ​​to 32-bit values.

[0019] The compressed and stored image data can be decompressed for reconstruction (e.g., in response to a user command) such that each region (e.g., 8-bit or 16-bit) is read from storage and rescaled according to one or more values ​​(e.g., 32-bit floating-point values). In some examples, each region is rescaled to a single value regardless of the compressed value. Thus, a region having both an 8-bit compressed value and a 16-bit compressed value is rescaled to a 32-bit value for presentation.

[0020] Advantageously, images stored in accordance with the disclosed systems and methods exhibit a significant reduction in image quantization, resulting in standardized read / writes, reduced storage requirements (e.g., from overall initial 32-bit values ​​to scaled 8-bit or 16-bit values), increased reconstruction speed, and efficient data management of scaled image data.

[0021] Therefore, as disclosed herein, localized scaling / rescaling of image data (based on dynamic scaling of the initial image data) results in a higher dynamic range than conventional 8-bit or 16-bit conversion techniques.

[0022] In a disclosed example, a method for compressing digital image data of an object includes identifying a plurality of regions in a digital image of the object, assigning a first bit value to a first region of the plurality of regions, compressing digital image data associated with the first region based on the assigned first bit value, assigning a second bit value to a second region of the plurality of regions, compressing digital image data associated with the second region based on the assigned second bit value, and storing the compressed digital image data associated with the first region and the second region on a storage medium.

[0023] In some examples, the method further includes accessing the storage medium, assigning a third bit value to a first region and a second region of the plurality of regions, decompressing compressed digital image data associated with the first region and the second region based on the assigned third bit value, and reconstructing a digital image of the object based on the third bit value.

[0024] In some examples, the method further includes presenting the digital image to a user via one or more output devices.

[0025] In some examples, the first bit value is 8 bits. In some examples, the second bit value is 16 bits. In some examples, the third bit value is 32 bits. In some examples, the digital image is composed of a plurality of voxels or pixels. In some examples, each voxel or pixel of the plurality of voxels corresponds to one region of the plurality of regions. In some examples, the size of each voxel or pixel ranges from tens of micrometers to hundreds of micrometers. In some examples, each region of the plurality of regions corresponds to one voxel of the plurality of voxels or one pixel of the plurality of pixels.

[0026] In some examples, the method further includes scanning the object with a radiation-emitting source to generate a digital image of the object.

[0027] In some examples, the first bit value and the second bit value are a common bit value.

[0028] In some examples, the method further includes assigning a fourth bit value to a third region of the plurality of regions, compressing the digital image data associated with the third region based on the assigned fourth bit value, and storing the compressed digital image data associated with the third region.

[0029] In some disclosed examples, an industrial imaging system includes an adjustable fixture configured to position an object, a detector configured to capture radiation from the object, and an image acquisition system configured to generate an image of the object based on the detected radiation, the image acquisition system including a user interface with an input device, processing circuitry, and memory circuitry containing machine-readable instructions that, when executed by the processing circuitry, cause the processing circuitry to receive digital image data corresponding to the object via the detector, identify a plurality of regions in the digital image, receive via the input device a first bit-depth selection value for a first region of the plurality of regions, receive via the input device a second bit-depth selection value for a second region of the plurality of regions, compress the digital image data associated with the first region based on the assigned first bit-depth, compress the digital image data associated with the second region based on the assigned second bit-depth, and store the compressed digital image data associated with the first region and the second region in the memory circuitry.

[0030] In some examples, the processing circuitry is further operable to store the compressed digital image data associated with the second region on a storage medium.

[0031] In some examples, a radiation emitter transmits radiation toward an object to be received by a detector.

[0032] In some examples, the detector is a sensor panel comprising one or more of a charge coupled device (CCD) panel or a complementary metal-oxide-semiconductor (CMOS) panel.

[0033] In some disclosed examples, a method for compressing digital image data of an object includes identifying a plurality of regions in a digital image of the object, determining one or more characteristics of each identified region of the plurality of regions, assigning a bit value to each identified region of the plurality of regions based on the one or more characteristics, compressing digital image data associated with each identified region based on the assigned bit value, and storing the compressed digital image data associated with each identified region on a storage medium.

[0034] In some disclosed examples, the method further includes comparing the determined one or more characteristics to a list associating characteristic data with desired bit values, and determining an assigned bit value based on the comparison.

[0035] 1 illustrates an exemplary imaging device 100. In some examples, the imaging device 100 may be an X-ray radiographer 100 that can be used for non-destructive testing (NDT), digital radiography (DR) scans, computerized tomography (CT) scans, and / or other applications performed on an object 102. Although some examples are provided with respect to imaging systems such as industrial imaging system 100, the compression and / or decompression systems and methods disclosed herein are generally applicable to image data, regardless of the source of the image data or the technique used to acquire the image data.

[0036] In some examples, the object 102 may be an industrial component and / or assembly of components (e.g., engine castings, microchips, bolts, etc.). In some examples, the object 102 may be relatively small such that finer, more detailed, and higher resolution radiographic imaging processes may be useful. Although some examples are discussed with respect to X-rays for simplicity, in some examples, the industrial X-ray radiographer 100 discussed herein may also use radiation at other wavelengths (e.g., gamma rays, neutrons, etc.).

[0037] 1 , imaging device 100 directs radiation 104 (e.g., X-rays) from emitter 106 through object 102 to detector 108. In some examples, a two-dimensional (2D) digital image (e.g., a radiographic image, an X-ray image, etc.) can be generated based on radiation 104 incident on detector 108. In some examples, the 2D image can be generated by detector 108 itself. In some examples, the 2D image can be generated by combining detector 108 with a computing system in communication with detector 108.

[0038] In some examples, the 2D images may be continuously captured / acquired at a given frame rate by the detector 108 (e.g., in a free-running mode) as long as the detector 108 is powered on. However, in some examples, the 2D images may only be generated entirely by the detector 108 (and / or associated computing system(s)) when a scanning / imaging process is selected and / or operating. Similarly, in some examples, the 2D images may be stored in persistent (i.e., non-volatile) memory when a scanning / imaging process is selected and / or operating.

[0039] In some examples, 2D images generated by the detector 108 (and / or associated computing system(s)) may be combined to form a three-dimensional (3D) volume and / or 3D image containing voxels. In some examples, 2D image slices of the 3D volume / image may also be formed. Note that while the term "image" is used herein as shorthand, an "image" may include representative data 102 until that data is visually rendered by one or more appropriate components (e.g., a display screen, a graphics processing unit, the detector 108, etc.).

[0040] In some examples, the detector 108 can include a flat panel detector (FDA), a linear diode array (LDA), and / or a lens-coupled scintillation detector. In some examples, the detector 108 can include a fluoroscopic detection system and / or a digital image sensor configured to receive images indirectly via scintillation. In some examples, the detector 108 can be implemented using a sensor panel (e.g., a charge-coupled device (CCD) panel, a complementary metal-oxide semiconductor (CMOS) panel, etc.) configured to directly receive x-rays and generate a digital image. In some examples, the detector 108 can include a scintillation layer / screen that absorbs radiation and emits visible light photons, which are then detected by a solid-state detector panel (e.g., a CMOS panel and / or a CCD panel) coupled to the scintillation screen.

[0041] In some examples, the detector 108 (e.g., a solid-state detector panel) can include pixels 404 (see, e.g., FIGS. 4A and 4B). In some examples, the pixels 404 can correspond to portions of a scintillation screen. In some examples, the size of each pixel 404 can range from tens of micrometers to hundreds of micrometers. In some examples, the pixel size, and therefore the voxel size, of the detector 108 can be in the range of 25 micrometers to 250 micrometers (e.g., 200 micrometers), although nanometer-scale pixel and / or voxel sizes or pixel and / or voxel sizes greater than 250 micrometers are also possible.

[0042] In some examples, a 2D image captured by detector 108 (and / or an associated computing system) may include features finer (e.g., smaller features, denser features, etc.) than the pixel size of detector 108. For example, a computer microchip may have very fine features that are smaller than pixels 404. In such examples, it may be useful to use sub-pixel sampling to achieve higher and more detailed resolution than would be possible without using sub-pixel sampling.

[0043] For example, multiple 2D images of the object 102 can be captured while the object 102 is at the same orientation and the detector 108 is at one of two (or more) different positions. In some examples, the different positions of the detector 108 can be offset from one another by an amount less than the size of a pixel 404 (i.e., a sub-pixel). The multiple sub-pixel shifted 2D images can then be combined (e.g., via interlacing techniques) to form a single higher resolution 2D image of the object 102 at that orientation. Thus, when the term “high resolution imaging process” is used herein, this term can refer to an imaging process (e.g., radiography, computed tomography, etc.) that uses sub-pixel sampling to ensure a resolution (and / or pixel density) of the final image that is greater than the resolution (and / or pixel density) of the detector 108 (and / or portions of the detector 108 and / or virtual detector) used to capture the image. It may be possible to instead translate the object 102 rather than the detector 108 to perform sub-pixel sampling, but moving the object 102 may change the imaging geometry, which may adversely affect the resulting image combination.

[0044] 1, imaging apparatus 100 includes a detector positioner 150 configured to move detector 108 to different detector positions (e.g., positions for sub-pixel sampling). As shown, detector positioner 150 includes two parallel posts 152 connected by two parallel rails 154. As shown, detector 108 is held on rails 154. In some examples, detector 108 can be held on rails 154 (and / or attached to rails 154) by one or more intermediate supports.

[0045] Just as detector 108 can be moved by detector positioner 150, in some examples, object 102 can be moved by object positioner 110. In the example of FIG. 1 , object positioner 110 includes a rotatable fixture 112 on which object 102 is positioned. As shown, rotatable fixture 112 is a circular plate. As shown, rotatable fixture 112 is attached to a motorized spindle 116, through which rotatable fixture 112 can rotate about an axis defined by spindle 116. In the example of FIG. 1 , rotatable fixture 112 is supported by support structure 118. In some examples, support structure 118 can be configured to translate rotatable fixture 112 (and / or object 102) toward and / or away from emitter 106 and / or detector 108. In some examples, the support structure 118 can include one or more actuators configured to impart the translation(s).

[0046] Figure 2 illustrates an example of an imaging system 200 that includes an imaging device, such as, for example, the example imaging device 100 shown in Figure 1. As shown, the imaging system 200 also includes a computing system 202, a user interface (UI) 204, and a remote computing system 299. Although only one imaging device 100, computing system 202, UI 204, and remote computing system 299 is shown in the example of Figure 2, in some examples, the system 200 can include multiple imaging devices 100, computing systems 202, UIs 204, and / or remote computing systems 299.

[0047] 2, imaging device 100 includes emitter 106, detector 108, detector positioner 150, and object positioner 110 enclosed within housing 199. As shown, imaging device 100 is connected to and / or in communication with computing system(s) 202 and UI(s) 204. In some examples, imaging system 100 may also be in electrical communication with remote computing system(s) 299. In some examples, communication and / or connection may be electrical, electromagnetic, wired, and / or wireless.

[0048] 2, the UI 204 includes one or more input devices 206 and / or output devices 208. In some examples, the one or more input devices 206 may include one or more touchscreens, mice, keyboards, buttons, switches, slides, knobs, microphones, dials, and / or other electromechanical input devices. In some examples, the one or more output devices 208 may include one or more display screens, speakers, lights, tactile devices, and / or other devices. In some examples, a user may provide input to and / or receive output from the imaging device(s) 100, the computing system(s) 202, and / or the remote computing system(s) 299 via the UI(s) 204.

[0049] In some examples, the UI(s) 204 can be part of the computing system 202. In some examples, the computing system 202 can implement one or more controllers of the imaging device(s) 100. In some examples, the computing system 202, together with the UI(s) 204, can comprise the image acquisition system of the imaging system 200. In some examples, the remote computing system(s) 299 can be similar to or identical to the computing system 202.

[0050] 2, computing system 202 is in communication (e.g., electrically) with imaging device(s) 100, UI(s) 204, and remote computing system(s) 299. In some examples, communication may be direct communication (e.g., via wired and / or wireless media) or indirect communication, such as via one or more wired and / or wireless networks (e.g., local area networks and / or wide area networks). As shown, computing system 202 includes processing circuitry 210 (which may include a graphics processing unit (GPU)), memory circuitry 212, and communication circuitry 214 interconnected with each other via a common electrical bus.

[0051] In some examples, processing circuitry 210 may include one or more processors. In some examples, communications circuitry 214 may include one or more wireless adapters, wireless cards, cable adapters, wired adapters, radio frequency (RF) devices, wireless communication devices, Bluetooth devices, IEEE 802.11-compliant devices, WiFi devices, cellular devices, GPS devices, Ethernet ports, network ports, Lightning cable ports, cable ports, etc. In some examples, communications circuitry 214 may be configured to facilitate communication over one or more wired media and / or protocols (e.g., Ethernet cable(s), Universal Serial Bus cable(s), etc.) and / or wireless media and / or protocols (e.g., Near Field Communication (NFC), Very High Frequency Radio (commonly known as Bluetooth), IEEE 802.11x, Zigbee, HART, LTE, Z-Wave, WirelessHD, WiGig, etc.).

[0052] 2, memory circuitry 212 includes and / or stores image data scaling processes 500, 530 (shown, for example, in FIGS. 5A and 5B). In some examples, image data scaling processes 500, 530 may be implemented via machine-readable (and / or processor-executable) instructions stored in memory circuitry 212 and / or executed by processing circuitry 210. In some examples, image data scaling processes 500, 530 may be performed as part of a larger scanning and / or imaging process of imaging system 200.

[0053] 3A and 3B illustrate the concept of identifying multiple regions 304 (or voxels) within an image 302 (e.g., a three-dimensional image) and assigning dynamic scaling values ​​to the different regions. In particular, the multiple regions 304 may correspond to voxels representing values ​​in a cubic grid arranged in three-dimensional space, as shown in image data 302 of FIGS. 3A and 3B.

[0054] As shown, the diagram represents the image data as a rectangular prism having a first grid of regions 304a, a second grid of regions 304b, and a third grid of regions 304c. In the example of Figures 3A and 3B, each region corresponds to a voxel having a defined size value 300. Although the image data is represented as a rectangular prism, image data from any geometric shape of varying complexity can be subjected to the compression / reconstruction techniques disclosed herein.

[0055] 3B, one or more regions or voxels 304 can be identified as exhibiting one or more characteristics corresponding to scaling values. As shown, first regions 304a, 304b, and 304c are identified as exhibiting a first characteristic (e.g., a first density value, a first contrast value, etc.), while second regions 304a1, 304b1, and 304c1 (shown with shading for clarity) are identified as exhibiting a second characteristic (e.g., a second density value, a second contrast value, etc.). A first scaling value (e.g., an 8-bit depth or 8-bit value) can then be assigned (e.g., via processing circuitry 210) to the first region, while a second scaling value (e.g., a 16-bit depth or 16-bit value) can be assigned to the second region.

[0056] Although some examples are provided with respect to three-dimensional images (corresponding to models of three-dimensional objects), the principles and techniques disclosed herein are equally applicable to two-dimensional images. As shown in Figures 4A and 4B, the disclosed compression / reconstruction techniques described herein are applied to pixels within image data.

[0057] 4A, multiple regions 404 (or pixels) within an image 402 (e.g., a two-dimensional image) and assigning dynamic scaling values ​​to the different regions. In particular, as shown in image data 302 of FIGS. 4A and 4B, the multiple regions 304 may correspond to pixels representing values ​​in a square grid arranged in two-dimensional space.

[0058] As shown, the diagram represents image data as squares in a grid of regions 404, each having a defined size value 400. As shown in FIG. 4B, one or more regions or pixels 404 can be identified as exhibiting one or more characteristics corresponding to scaling values. As shown, a first region 404 is identified as exhibiting a first characteristic (e.g., a first density value, a first contrast value, etc.), while a second region 404a (shown with shading for clarity) is identified as exhibiting a second characteristic (e.g., a second density value, a second contrast value, etc.). A first scaling value (e.g., an 8-bit depth or 8-bit value) can then be assigned (e.g., via processing circuitry 210) to the first region, while a second scaling value (e.g., a 16-bit depth or 16-bit value) can be assigned to the second region(s).

[0059] 5A is a flowchart illustrating an example operation of a digital image data scaling process 500. In the example of FIG. 5A, the digital image data scaling process 500 (e.g., compression, decompression, and / or reconstruction) begins at block 502. At block 504, the process 500 identifies multiple regions in a digital image of an object. For example, each region may correspond to a voxel or pixel, and each region may be of equal size.

[0060] One or more characteristics of each identified region are determined at block 506. For example, at block 508, data corresponding to a given region can be compared to a list of characteristics associating characteristic data with a desired scaling value, depth, volume, or size (e.g., a desired compression value). In some examples, the list is stored in memory circuit 212, but can additionally or alternatively be stored on remote computing system 299.

[0061] Based on the comparison, a scaling value, depth, volume, or size corresponding to each region is determined in block 510. For example, a first scaling value, first depth, first volume, or first size may be determined for a region having a first characteristic, and a second value, second depth, second volume, or second size may be determined for a region having a second characteristic. In some examples, the scaling value is a bit value or bit depth of a voxel and / or pixel in the image data.

[0062] At block 512, a first scaling value, a first depth, a first volume, or a first size (e.g., bit value) is assigned to a first region of the plurality of regions, and at block 514, a second scaling value, a second depth, a second volume, or a second size (e.g., bit value) is assigned to a second region of the plurality of regions. Although certain examples are provided describing the plurality of regions in the image data as being divisible as first regions and second regions, the image data is not limited to two regions or two types of regions. For example, there can be three, four, or more regions (e.g., an unlimited number of regions), each of which can be assigned a common scaling value or any of a variety of scaling values ​​(e.g., based on one or more characteristics of each region).

[0063] At block 516, the digital image data associated with the first region and the second region is scaled / compressed based on the corresponding scaling values. At block 518, the scaled / compressed digital image data associated with the first region and the second region is written to a storage medium (e.g., memory circuit 212, remote computing system 299).

[0064] The scaled and stored image data in FIG. 5A can be decompressed and reconstructed in the flowchart of FIG. 5B, which illustrates an example digital image data scaling process 530. In the example of FIG. 5B, the digital image data scaling process 530 (e.g., decompression and / or reconstruction) begins at block 532. At block 532, the system receives a command to reconstruct the compressed and stored image data, such as via the UI 204 or the remote computing system 299. At block 534, the storage medium is accessed to scale the image data. At block 536, a third scaling value is assigned to both the first region and the second region, such that image data associated with both regions is decompressed at block 538. Once the image data is scaled to the third scaling value (e.g., from 8-bit or 16-bit values ​​to 32-bit values), a digital image of the object is reconstructed at block 540. In some examples, the reconstructed digital image is presented, such as on a display, at block 542. The process ends at block 544.

[0065] The methods and / or systems can be implemented in hardware, software, and / or a combination of hardware and software. The methods and / or systems can be implemented centrally in at least one computing system, or in a distributed fashion where different elements are distributed across several interconnected and / or remote computing systems. Any kind of computing system or other apparatus adapted for performing the methods described herein is suitable. A typical combination of hardware and software can include a general-purpose computing system, along with a program or other code that, when loaded and executed, controls the computing system to perform the methods described herein. Another typical embodiment can include an application-specific integrated circuit or chip. Some embodiments can include a non-transitory machine-readable (e.g., computer-readable) medium (e.g., a flash drive, an optical disk, a magnetic storage disk, etc.) that stores one or more instructions (e.g., lines of code) executable by a machine, thereby causing the machine to perform a process as described herein.

[0066] While the present method and / or system has been described with reference to certain specific embodiments, those skilled in the art will recognize that various modifications can be made and equivalents can be substituted without departing from the scope of the present method and / or system. In addition, many modifications can be made to adapt a particular situation or material to the teachings of the disclosure without departing from the scope of the disclosure. Therefore, the present method and / or system is not limited to the particular embodiments disclosed, but it is intended that the present method and / or system include all embodiments falling within the scope of the appended claims.

[0067] As used herein, "and / or" means any one or more of the items in the list connected by "and / or." As an example, "x and / or y" means any element of the three-element set {(x), (y), (x,y)}. In other words, "x and / or y" means "one or both of x and y." As another example, "x, y and / or z" means any element of the seven-element set {(x), (y), (z), (x,y), (x,z), (y,z), (x,y,z)}. In other words, "x, y and / or z" means "one or more of x, y and z."

[0068] As used herein, the term "for example" begins a list of one or more non-limiting examples, instances, or illustrations.

[0069] As used herein, the terms "coupled," "coupled to," and "coupled with" mean a structural and / or electrical connection, whether attached, attached, connected, joined, fastened, linked, and / or otherwise secured. As used herein, the term "attach" means attached, attached, connected, joined, fastened, linked, and / or otherwise secured. As used herein, the term "connect" means attached, attached, connected, joined, fastened, linked, and / or otherwise secured.

[0070] As used herein, the terms “circuitry” and “circuitry” refer to physical electronic components (i.e., hardware) and any software and / or firmware (“code”) that can comprise, be executed by, and / or be otherwise associated with hardware. As used herein, for example, a particular processor and memory can comprise a first “circuit” when executing a first one or more lines of code, and can comprise a second “circuit” when executing a second one or more lines of code. As used herein, whenever circuitry includes the hardware and / or code (if either is necessary) necessary to perform a function, the circuitry is “operable” and / or “configured” to perform that function, regardless of whether performance of that function is disabled or enabled (e.g., by a user-configurable setting, factory trim, etc.).

[0071] As used herein, control circuitry can include digital and / or analog circuitry, discrete and / or integrated circuitry, microprocessors, DSPs, etc., software, hardware, and / or firmware located on one or more boards that form part or all of a controller and / or are used to control the welding process and / or devices such as power supplies or wire feeders.

[0072] As used herein, the term "processor" refers to processing devices, apparatus, programs, circuits, components, systems, and subsystems, whether implemented in hardware, tangibly embodied software, or both, and whether programmable or not. As used herein, the term "processor" includes, but is not limited to, one or more computing devices, hardwired circuits, signal-modifying devices and systems, system-controlling devices and machines, central processing units, programmable devices and systems, field-programmable gate arrays, application-specific integrated circuits, systems-on-chips, systems comprising discrete elements and / or circuits, state machines, virtual machines, data processors, processing facilities, and any combination of the above. A processor may be, for example, any type of general-purpose microprocessor or microcontroller, a digital signal processing (DSP) processor, an application-specific integrated circuit (ASIC), a graphics processing unit (GPU), an advanced reduced instruction set computer (RISC) processor with an ARM core, etc. A processor may be coupled to and / or integrated into a memory device.

[0073] As used herein, the terms "memory" and / or "memory device" refer to computer hardware or circuitry that stores information for use by a processor and / or other digital device. The memory and / or memory device can be any suitable type of computer memory or any other type of electronic storage medium, such as read-only memory (ROM), random access memory (RAM), cache memory, compact disc read-only memory (CDROM), electro-optical memory, magneto-optical memory, programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), computer-readable medium, etc. Memory may include, for example, non-transitory memory, non-transitory processor-readable medium, non-transitory computer-readable medium, non-volatile memory, dynamic RAM (DRAM), volatile memory, ferroelectric RAM (FRAM), first-in-first-out (FIFO) memory, last-in-first-out (LIFO) memory, stacked memory, non-volatile RAM (NVRAM), static RAM (SRAM), cache, buffer, semiconductor memory, magnetic memory, optical memory, flash memory, flash card, compact flash card, memory card, secure digital memory card, micro card, mini card, expansion card, smart card, memory stick, multimedia card, picture card, flash storage, subscriber identity module (SIM) card, hard drive (HDD), solid state drive (SSD), etc. Memory may be configured to store code, instructions, applications, software, firmware, and / or data and may be external, internal, or both to the processor.

Claims

1. 1. A method for compressing digital image data of an object, comprising: identifying a plurality of regions in the digital image of the object; assigning a first bit value to a first region of the plurality of regions; compressing the digital image data associated with the first region based on the assigned first bit value; assigning a second bit value to a second region of the plurality of regions; compressing the digital image data associated with the second region based on the assigned second bit value; storing the compressed digital image data associated with the first region and the second region on a storage medium; A method comprising:

2. accessing the storage medium; assigning a third bit value to the first region and the second region of the plurality of regions; decompressing the compressed digital image data associated with the first region and the second region based on the assigned third bit value; reconstructing the digital image of the object based on the third bit values; The method of claim 1 further comprising:

3. The method of claim 1 , further comprising presenting the digital image to a user via one or more output devices.

4. The method of claim 1 , wherein the first bit value is 8 bits.

5. The method of claim 1 , wherein the second bit value is 16 bits.

6. The method of claim 1 , wherein the third bit value is 32 bits.

7. The method of claim 1 , wherein the digital image is composed of a plurality of voxels or pixels.

8. The method of claim 7 , wherein each voxel or pixel of the plurality of voxels corresponds to one of the plurality of regions.

9. The method of claim 7 , wherein the size of each voxel or pixel ranges from tens of micrometers to hundreds of micrometers.

10. The method of claim 7 , wherein each region of the plurality of regions corresponds to a voxel of the plurality of voxels or a pixel of the plurality of pixels.

11. The method of claim 1 , further comprising scanning the object with a radiation-emitting source to generate a digital image of the object.

12. The method of claim 1 , wherein the first bit value and the second bit value are a common bit value.

13. assigning a fourth bit value to a third region of the plurality of regions; compressing the digital image data associated with the third region based on the assigned fourth bit value; storing the compressed digital image data associated with the third region; The method of claim 1 further comprising:

14. an adjustable fixture configured to position the object; a detector configured to capture radiation from the object; an image acquisition system configured to generate an image of the object based on the detected radiation; Equipped with the image acquisition system a user interface having an input device; a processing circuit unit; a memory circuitry that, when executed by the processing circuitry, receiving digital image data corresponding to the object via the detector; identifying a plurality of regions in the digital image; receiving, via the input device, a first bit depth selection for a first region of the plurality of regions; receiving, via the input device, a second bit depth selection for a second region of the plurality of regions; compressing the digital image data associated with the first region based on the assigned first bit depth; compressing the digital image data associated with the second region based on the assigned second bit depth; storing the compressed digital image data associated with the first region and the second region in the memory circuitry; memory circuitry containing machine-readable instructions that cause said processing circuitry to:

1. An industrial imaging system comprising:

15. 15. The system of claim 14, wherein the processing circuitry is further operable to store the compressed digital image data associated with the second region on the storage medium.

16. The system of claim 14 , further comprising a radiation emitter that transmits radiation toward the object to be received by the detector.

17. 15. The system of claim 14, wherein the detector is a sensor panel comprising one or more of a charge-coupled device (CCD) panel or a complementary metal-oxide semiconductor (CMOS) panel.

18. 1. A method for compressing digital image data of an object, comprising: identifying a plurality of regions in the digital image of the object; determining one or more characteristics of each identified region of the plurality of regions; assigning a bit value to each identified region of the plurality of regions based on the one or more characteristics; compressing the digital image data associated with each identified region based on the assigned bit value; storing the compressed digital image data associated with each identified region on a storage medium; A method comprising:

19. comparing the determined one or more characteristics to a list associating characteristic data with desired bit values; determining the assigned bit value based on the comparison; and 20. The method of claim 18, further comprising:

20. 20. The method of claim 19, wherein one of the assigned bit value or the desired bit value is 8 bits, 16 bits, or 32 bits.

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