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It involves arranging the image components in a ” zigzag ” order employing run-length encoding RLE algorithm that groups similar frequencies together, inserting length coding zeros, and then using Huffman coding on what is left. The JPEG standard also allows, but does not require, decoders to support the use of arithmetic coding , which is mathematically superior to Huffman coding. However, this feature has rarely been used, as it was historically covered by patents requiring royalty-bearing licenses, and because it is slower to encode and decode compared to Huffman coding.

The previous quantized DC coefficient is used to predict the current quantized DC coefficient. The difference between the two is encoded rather than the actual value. The encoding of the 63 quantized AC coefficients does not use such prediction differencing. The zigzag sequence for the above quantized coefficients are shown below. This encoding mode is called baseline sequential encoding.

Baseline JPEG also supports progressive encoding. While sequential encoding encodes coefficients of a single block at a time in a zigzag manner , progressive encoding encodes similar-positioned batch of coefficients of all blocks in one go called a scan , followed by the next batch of coefficients of all blocks, and so on.

Once all similar-positioned coefficients have been encoded, the next position to be encoded is the one occurring next in the zigzag traversal as indicated in the figure above. It has been found that baseline progressive JPEG encoding usually gives better compression as compared to baseline sequential JPEG due to the ability to use different Huffman tables see below tailored for different frequencies on each “scan” or “pass” which includes similar-positioned coefficients , though the difference is not too large.

In the rest of the article, it is assumed that the coefficient pattern generated is due to sequential mode. The JPEG standard provides general-purpose Huffman tables; encoders may also choose to generate Huffman tables optimized for the actual frequency distributions in images being encoded. The process of encoding the zig-zag quantized data begins with a run-length encoding explained below, where:. The run-length encoding works by examining each non-zero AC coefficient x and determining how many zeroes came before the previous AC coefficient.

With this information, two symbols are created:. The higher bits deal with the number of zeroes, while the lower bits denote the number of bits necessary to encode the value of x. This has the immediate implication of Symbol 1 being only able store information regarding the first 15 zeroes preceding the non-zero AC coefficient. One is for ending the sequence prematurely when the remaining coefficients are zero called “End-of-Block” or “EOB” , and another when the run of zeroes goes beyond 15 before reaching a non-zero AC coefficient.

In such a case where 16 zeroes are encountered before a given non-zero AC coefficient, Symbol 1 is encoded “specially” as: 15, 0 0. The overall process continues until “EOB” — denoted by 0, 0 — is reached. See above. From here, frequency calculations are made based on occurrences of the coefficients. In our example block, most of the quantized coefficients are small numbers that are not preceded immediately by a zero coefficient.

These more-frequent cases will be represented by shorter code words. The resulting compression ratio can be varied according to need by being more or less aggressive in the divisors used in the quantization phase.

Ten to one compression usually results in an image that cannot be distinguished by eye from the original. A compression ratio of is usually possible, but will look distinctly artifacted compared to the original.

The appropriate level of compression depends on the use to which the image will be put. Those who use the World Wide Web may be familiar with the irregularities known as compression artifacts that appear in JPEG images, which may take the form of noise around contrasting edges especially curves and corners , or “blocky” images. These are due to the quantization step of the JPEG algorithm. They are especially noticeable around sharp corners between contrasting colors text is a good example, as it contains many such corners.

The analogous artifacts in MPEG video are referred to as mosquito noise , as the resulting “edge busyness” and spurious dots, which change over time, resemble mosquitoes swarming around the object.

These artifacts can be reduced by choosing a lower level of compression ; they may be completely avoided by saving an image using a lossless file format, though this will result in a larger file size.

The images created with ray-tracing programs have noticeable blocky shapes on the terrain. Certain low-intensity compression artifacts might be acceptable when simply viewing the images, but can be emphasized if the image is subsequently processed, usually resulting in unacceptable quality.

Consider the example below, demonstrating the effect of lossy compression on an edge detection processing step. Some programs allow the user to vary the amount by which individual blocks are compressed.

Stronger compression is applied to areas of the image that show fewer artifacts. This way it is possible to manually reduce JPEG file size with less loss of quality. Since the quantization stage always results in a loss of information, JPEG standard is always a lossy compression codec.

Information is lost both in quantizing and rounding of the floating-point numbers. Even if the quantization matrix is a matrix of ones , information will still be lost in the rounding step. Rounding the output to integer values since the original had integer values results in an image with values still shifted down by This is the decompressed subimage. If this occurs, the decoder needs to clip the output values so as to keep them within that range to prevent overflow when storing the decompressed image with the original bit depth.

The error is most noticeable in the bottom-left corner where the bottom-left pixel becomes darker than the pixel to its immediate right. These requirements are specified in ITU. T Recommendation T. For example, the output of a decoder implementation must not exceed an error of one quantization unit in the DCT domain when applied to the reference testing codestreams provided as part of the above standard. While unusual, and unlike many other and more modern standards, ITU.

JPEG compression artifacts blend well into photographs with detailed non-uniform textures, allowing higher compression ratios. Notice how a higher compression ratio first affects the high-frequency textures in the upper-left corner of the image, and how the contrasting lines become more fuzzy.

The very high compression ratio severely affects the quality of the image, although the overall colors and image form are still recognizable. However, the precision of colors suffer less for a human eye than the precision of contours based on luminance. This justifies the fact that images should be first transformed in a color model separating the luminance from the chromatic information, before subsampling the chromatic planes which may also use lower quality quantization in order to preserve the precision of the luminance plane with more information bits.

For information, the uncompressed bit RGB bitmap image below 73, pixels would require , bytes excluding all other information headers. The filesizes indicated below include the internal JPEG information headers and some metadata. On grayscale images, a minimum of 6. For most applications, the quality factor should not go below 0. The image at lowest quality uses only 0. This is useful when the image will be displayed in a significantly scaled-down size. The medium quality photo uses only 4.

However, once a certain threshold of compression is passed, compressed images show increasingly visible defects. See the article on rate—distortion theory for a mathematical explanation of this threshold effect. More modern designs such as JPEG and JPEG XR exhibit a more graceful degradation of quality as the bit usage decreases — by using transforms with a larger spatial extent for the lower frequency coefficients and by using overlapping transform basis functions.

From to , new research emerged on ways to further compress the data contained in JPEG images without modifying the represented image.

Standard general-purpose compression tools cannot significantly compress JPEG files. Typically, such schemes take advantage of improvements to the naive scheme for coding DCT coefficients, which fails to take into account:. Some standard but rarely used options already exist in JPEG to improve the efficiency of coding DCT coefficients: the arithmetic coding option, and the progressive coding option which produces lower bitrates because values for each coefficient are coded independently, and each coefficient has a significantly different distribution.

Modern methods have improved on these techniques by reordering coefficients to group coefficients of larger magnitude together; [55] using adjacent coefficients and blocks to predict new coefficient values; [57] dividing blocks or coefficients up among a small number of independently coded models based on their statistics and adjacent values; [56] [57] and most recently, by decoding blocks, predicting subsequent blocks in the spatial domain, and then encoding these to generate predictions for DCT coefficients.

It contains two static images, one for the left eye and one for the right eye; encoded as two side-by-side images in a single JPG file. This file format can be viewed as a JPEG without any special software, or can be processed for rendering in other modes. It contains two or more JPEG files concatenated together. Other devices use it to store “preview images” that can be displayed on a TV.

In the last few years, due to the growing use of stereoscopic images, much effort has been spent by the scientific community to develop algorithms for stereoscopic image compression. It was first published in and was key for the success of the standard. In March , Google released the open source project Guetzli , which trades off a much longer encoding time for smaller file size similar to what Zopfli does for PNG and other lossless data formats.

Extension layers are used to modify the JPEG 8-bit base layer and restore the high-resolution image. Existing software is forward compatible and can read the JPEG XT binary stream, though it would only decode the base 8-bit layer. The standard should also offer higher bit depths 12—16 bit integer and floating point , additional color spaces and transfer functions such as Log C from Arri , embedded preview images, lossless alpha channel encoding, image region coding, and low-complexity encoding.

Any patented technologies would be licensed on a royalty-free basis. The proposals were submitted by September , leading to a committee draft in July , with file format and core coding system were formally standardized on 13 October and 30 March respectively. From Wikipedia, the free encyclopedia. This is the latest accepted revision , reviewed on 1 August Lossy compression method for reducing the size of digital images. A photo of a European wildcat with the compression rate decreasing and hence quality increasing, from left to right.

Left: a final image is built up from a series of basis functions. Right: each of the DCT basis functions that comprise the image, and the corresponding weighting coefficient. Middle: the basis function, after multiplication by the coefficient: this component is added to the final image.

Main article: Entropy encoding. Slight differences are noticeable between the original top and decompressed image bottom , which is most readily seen in the bottom-left corner. September Retrieved 12 July Collins English Dictionary. Retrieved The effects of video compression on acceptability of images for monitoring life sciences experiments Technical report. Journal of Electronic Imaging. S2CID BT Group. Namespaces Article Talk.

Views Read Edit View history. Help Learn to edit Community portal Recent changes Upload file. Download as PDF Printable version. ImageMagick License [1]. Converts PDF to raster images and vice versa. Apache License 2. Converts PostScript to other vector graphics file format. Public Domain. Supports merging, splitting, and extracting pages from PDFs.

Also rotating, deleting and reordering pages. Export documents only one page at a time pages can be later combined using PDF printer. Export PDF and many other formats, multi-pages and multi-layers. Apache OpenOffice Draw. All standard vector graphics editor features. Apache License. EPL 2. Formatting Objects Processor.

NET languages. Open source library to create and manipulate PDF files in Java. NET Framework 4 since v2. All OpenOffice. Mark-up language and tools to write technical reports, books, magazines, almost any publication type. Proprietary, freeware. Proprietary, adware. Allows users to add many elements to PDFs e. Retrieved May 15, December 9, Geophysical Research Letters.

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Dietary Reference Intakes for Calcium and Vitamin D |The National Academies Press.PDF Editor and Reader for Mac | Free Trial | PDF Expert

 
 

This guidance is intended to assist covered entities to understand what is de-identification, the general process by which de-identified information is created, and the options available for performing de-identification. In developing this guidance, the Office for Civil Rights OCR solicited input from stakeholders with practical, technical and policy experience in de-identification. OCR convened stakeholders at a workshop consisting of multiple panel sessions held March , , in Washington, DC.

The workshop was open to the public and each panel was followed by a question and answer period. Read the Full Guidance. Protected health information is information, including demographic information, which relates to:. By contrast, a health plan report that only noted the average age of health plan members was 45 years would not be PHI because that information, although developed by aggregating information from individual plan member records, does not identify any individual plan members and there is no reasonable basis to believe that it could be used to identify an individual.

The relationship with health information is fundamental. Identifying information alone, such as personal names, residential addresses, or phone numbers, would not necessarily be designated as PHI. For instance, if such information was reported as part of a publicly accessible data source, such as a phone book, then this information would not be PHI because it is not related to heath data see above.

If such information was listed with health condition, health care provision or payment data, such as an indication that the individual was treated at a certain clinic, then this information would be PHI. In general, the protections of the Privacy Rule apply to information held by covered entities and their business associates. HIPAA defines a covered entity as 1 a health care provider that conducts certain standard administrative and financial transactions in electronic form; 2 a health care clearinghouse; or 3 a health plan.

A covered entity may use a business associate to de-identify PHI on its behalf only to the extent such activity is authorized by their business associate agreement. The increasing adoption of health information technologies in the United States accelerates their potential to facilitate beneficial studies that combine large, complex data sets from multiple sources. The process of de-identification, by which identifiers are removed from the health information, mitigates privacy risks to individuals and thereby supports the secondary use of data for comparative effectiveness studies, policy assessment, life sciences research, and other endeavors.

The Privacy Rule was designed to protect individually identifiable health information through permitting only certain uses and disclosures of PHI provided by the Rule, or as authorized by the individual subject of the information. These provisions allow the entity to use and disclose information that neither identifies nor provides a reasonable basis to identify an individual. Both methods, even when properly applied, yield de-identified data that retains some risk of identification.

Although the risk is very small, it is not zero, and there is a possibility that de-identified data could be linked back to the identity of the patient to which it corresponds.

Regardless of the method by which de-identification is achieved, the Privacy Rule does not restrict the use or disclosure of de-identified health information, as it is no longer considered protected health information. Section Under this standard, health information is not individually identifiable if it does not identify an individual and if the covered entity has no reasonable basis to believe it can be used to identify an individual.

Health information that does not identify an individual and with respect to which there is no reasonable basis to believe that the information can be used to identify an individual is not individually identifiable health information. Sections As summarized in Figure 1, the Privacy Rule provides two methods by which health information can be designated as de-identified. Figure 1. A covered entity may determine that health information is not individually identifiable health information only if: 1 A person with appropriate knowledge of and experience with generally accepted statistical and scientific principles and methods for rendering information not individually identifiable: i Applying such principles and methods, determines that the risk is very small that the information could be used, alone or in combination with other reasonably available information, by an anticipated recipient to identify an individual who is a subject of the information; and ii Documents the methods and results of the analysis that justify such determination; or.

B All geographic subdivisions smaller than a state, including street address, city, county, precinct, ZIP code, and their equivalent geocodes, except for the initial three digits of the ZIP code if, according to the current publicly available data from the Bureau of the Census: 1 The geographic unit formed by combining all ZIP codes with the same three initial digits contains more than 20, people; and 2 The initial three digits of a ZIP code for all such geographic units containing 20, or fewer people is changed to C All elements of dates except year for dates that are directly related to an individual, including birth date, admission date, discharge date, death date, and all ages over 89 and all elements of dates including year indicative of such age, except that such ages and elements may be aggregated into a single category of age 90 or older.

De-identified health information created following these methods is no longer protected by the Privacy Rule because it does not fall within the definition of PHI. Of course, de-identification leads to information loss which may limit the usefulness of the resulting health information in certain circumstances.

As described in the forthcoming sections, covered entities may wish to select de-identification strategies that minimize such loss. The implementation specifications further provide direction with respect to re-identification , specifically the assignment of a unique code to the set of de-identified health information to permit re-identification by the covered entity.

If a covered entity or business associate successfully undertook an effort to identify the subject of de-identified information it maintained, the health information now related to a specific individual would again be protected by the Privacy Rule, as it would meet the definition of PHI. Disclosure of a code or other means of record identification designed to enable coded or otherwise de-identified information to be re-identified is also considered a disclosure of PHI. A covered entity may assign a code or other means of record identification to allow information de-identified under this section to be re-identified by the covered entity, provided that: 1 Derivation.

The code or other means of record identification is not derived from or related to information about the individual and is not otherwise capable of being translated so as to identify the individual; and 2 Security. The covered entity does not use or disclose the code or other means of record identification for any other purpose, and does not disclose the mechanism for re-identification.

The importance of documentation for which values in health data correspond to PHI, as well as the systems that manage PHI, for the de-identification process cannot be overstated. Esoteric notation, such as acronyms whose meaning are known to only a select few employees of a covered entity, and incomplete description may lead those overseeing a de-identification procedure to unnecessarily redact information or to fail to redact when necessary.

When sufficient documentation is provided, it is straightforward to redact the appropriate fields. See section 3. In the following two sections, we address questions regarding the Expert Determination method Section 2 and the Safe Harbor method Section 3. The notion of expert certification is not unique to the health care field. Professional scientists and statisticians in various fields routinely determine and accordingly mitigate risk prior to sharing data.

The field of statistical disclosure limitation, for instance, has been developed within government statistical agencies, such as the Bureau of the Census, and applied to protect numerous types of data. There is no specific professional degree or certification program for designating who is an expert at rendering health information de-identified.

Relevant expertise may be gained through various routes of education and experience. Experts may be found in the statistical, mathematical, or other scientific domains. From an enforcement perspective, OCR would review the relevant professional experience and academic or other training of the expert used by the covered entity, as well as actual experience of the expert using health information de-identification methodologies.

The ability of a recipient of information to identify an individual i. This is because the risk of identification that has been determined for one particular data set in the context of a specific environment may not be appropriate for the same data set in a different environment or a different data set in the same environment.

This issue is addressed in further depth in Section 2. The Privacy Rule does not explicitly require that an expiration date be attached to the determination that a data set, or the method that generated such a data set, is de-identified information.

However, experts have recognized that technology, social conditions, and the availability of information changes over time. Consequently, certain de-identification practitioners use the approach of time-limited certifications.

In this sense, the expert will assess the expected change of computational capability, as well as access to various data sources, and then determine an appropriate timeframe within which the health information will be considered reasonably protected from identification of an individual. Information that had previously been de-identified may still be adequately de-identified when the certification limit has been reached.

When the certification timeframe reaches its conclusion, it does not imply that the data which has already been disseminated is no longer sufficiently protected in accordance with the de-identification standard. Covered entities will need to have an expert examine whether future releases of the data to the same recipient e. In such cases, the expert must take care to ensure that the data sets cannot be combined to compromise the protections set in place through the mitigation strategy.

Of course, the expert must also reduce the risk that the data sets could be combined with prior versions of the de-identified dataset or with other publically available datasets to identify an individual. For instance, an expert may derive one data set that contains detailed geocodes and generalized aged values e. The expert may certify a covered entity to share both data sets after determining that the two data sets could not be merged to individually identify a patient.

This certification may be based on a technical proof regarding the inability to merge such data sets. Alternatively, the expert also could require additional safeguards through a data use agreement. No single universal solution addresses all privacy and identifiability issues. Rather, a combination of technical and policy procedures are often applied to the de-identification task. OCR does not require a particular process for an expert to use to reach a determination that the risk of identification is very small.

However, the Rule does require that the methods and results of the analysis that justify the determination be documented and made available to OCR upon request. The following information is meant to provide covered entities with a general understanding of the de-identification process applied by an expert.

It does not provide sufficient detail in statistical or scientific methods to serve as a substitute for working with an expert in de-identification.

A general workflow for expert determination is depicted in Figure 2. Stakeholder input suggests that the determination of identification risk can be a process that consists of a series of steps. First, the expert will evaluate the extent to which the health information can or cannot be identified by the anticipated recipients.

Second, the expert often will provide guidance to the covered entity or business associate on which statistical or scientific methods can be applied to the health information to mitigate the anticipated risk. The expert will then execute such methods as deemed acceptable by the covered entity or business associate data managers, i.

Finally, the expert will evaluate the identifiability of the resulting health information to confirm that the risk is no more than very small when disclosed to the anticipated recipients. Stakeholder input suggests that a process may require several iterations until the expert and data managers agree upon an acceptable solution. Regardless of the process or methods employed, the information must meet the very small risk specification requirement. Figure 2. Process for expert determination of de-Identification.

Data managers and administrators working with an expert to consider the risk of identification of a particular set of health information can look to the principles summarized in Table 1 for assistance. The principles should serve as a starting point for reasoning and are not meant to serve as a definitive list.

In the process, experts are advised to consider how data sources that are available to a recipient of health information e. Linkage is a process that requires the satisfaction of certain conditions. This is because of a second condition, which is the need for a naming data source, such as a publicly available voter registration database see Section 2. Without such a data source, there is no way to definitively link the de-identified health information to the corresponding patient.

Finally, for the third condition, we need a mechanism to relate the de-identified and identified data sources. The lack of a readily available naming data source does not imply that data are sufficiently protected from future identification, but it does indicate that it is harder to re-identify an individual, or group of individuals, given the data sources at hand. Example Scenario Imagine that a covered entity is considering sharing the information in the table to the left in Figure 3.

This table is devoid of explicit identifiers, such as personal names and Social Security Numbers. The information in this table is distinguishing, such that each row is unique on the combination of demographics i. Beyond this data, there exists a voter registration data source, which contains personal names, as well as demographics i. Linkage between the records in the tables is possible through the demographics.

Figure 3. Linking two data sources to identity diagnoses. Thus, an important aspect of identification risk assessment is the route by which health information can be linked to naming sources or sensitive knowledge can be inferred.