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This is done by simply dividing each component in the frequency domain by a constant for that component, and then rounding to the nearest integer.
This rounding operation is the only lossy operation in the whole process other than chroma subsampling if the DCT computation is performed with sufficiently high precision. As a result of this, it is typically the case that many of the higher frequency components are rounded to zero, and many of the rest become small positive or negative numbers, which take many fewer bits to represent.
The elements in the quantization matrix control the compression ratio, with larger values producing greater compression. Notice that most of the higher-frequency elements of the sub-block i. Entropy coding is a special form of lossless data compression. 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.
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