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<h2>A guide to PNG optimization</h2>

<h3>1. Background</h3>

<h4>1.1 The PNG file format</h4>

The <a href="http://www.libpng.org/pub/png/">Portable Network Graphics</a>
(PNG) is a format for storing compressed raster graphics. The
compression engine is based on the Deflate method
[<a href="http://www.ietf.org/rfc/rfc1951">RFC1951</a>],
designed by
<a href="http://www.pkware.com/">PKWare</a>
and originally used in PKZIP.

The PNG format is defined by the
<a href="http://www.libpng.org/pub/png/spec/">PNG Specification</a>.
This specification was developed by an ad-hoc group named the
<a href="http://www.libpng.org/pub/png/">PNG Development Group</a>, and it is
both an International Standard (published under the formal name
ISO/IEC&nbsp;15948) and a W3C Recommendation.

PNG was initially intended as a superior, patent-free replacement of GIF. The
final outcome is a modern, extensible, reliable image format, capable to handle
an impressive number of image types (from 1-bit black-and-white images up to
48-bit RGB images with a full 16-bit alpha channel), and geared by a
significantly stronger lossless compression engine (typically 5-25% better than
GIF).

Unlike other lossless compression schemes, PNG compression does not depend
solely on the statistics of the input, but it may vary within wide limits,
depending on the compressor's implementation. A good PNG encoder must be
able to take informed decisions about the factors that affect the size of the
output. The purpose of this article is to provide information about these
factors, and to give advice on implementing efficient PNG encoders.

<h4>1.2 The PNG compression</h4>

The PNG compression works in a pipeline manner.

In the first stage, the image pixels are passed through a lossless arithmetic
transformation named delta filtering, or simply
filtering, and sent further as a (filtered) byte sequence.
Filtering does not compress or otherwise reduce the size of the data, but it
makes the data more compressible.

In the second stage, the filtered byte sequence is passed through the
Ziv-Lempel algorithm (LZ77), producing LZ77 codes that are further compressed
by the Huffman algorithm in the third and final stage. The combination of the
last two stages is referred to as the Deflate compression, a
widely-used, patent-free algorithm for universal, lossless data compression.
The maximum size of the LZ77 sliding window in Deflate is 32768 bytes, and the
LZ77 matches can be between 3 and 258 bytes long.

A complete description of the PNG compression is beyond the scope of this
guide. The PNG Specification describes the format completely, and provides
a complete list of references to the underlying technologies.

<h3>2. Factors that affect the PNG file size</h3>

<div>
Like any other compression scheme, PNG compression depends on the statistics
of the input data. In addition, it depends on the following PNG-specific
parameters:
</div>
<ol>
<li>
 The PNG image type
</li>
<li>
 The PNG delta filters
</li>
<li>
 The strategy of searching LZ77 matches
</li>
<li>
 The size of the Huffman buffers inside the Deflate encoder
</li>
</ol>

Depending on how these parameters are chosen by the implementation, PNG
compression may vary within wide limits. The process of selecting the best
configuration is computationally infeasible, but heuristics to select a
satisfactory configuration are available. The problem of improving these
heuristics constitutes an interesting subject for research.

<h4>2.1 The PNG image type</h4>

The type of a PNG image is defined in the <code>IHDR</code> image
header. The image has a certain bit depth, up to 16 bits per sample, and a
certain color type, from Grayscale to RGB+Alpha. If two PNG files of different
types represent exactly the same image, each file can be regarded as a lossless
transformation of the other. A lossless transformation can reduce the
uncompressed stream, and such a transformation is named image
reduction. In most cases, image reductions are capable of reducing the
compressed stream (which is, in fact, our interest), as an indirect
effect of reducing the size of the compressor's input.

<div>
The possible image reductions are:
</div>
<ul>
<li>
 Bit depth reduction
 
 The bit depth can be reduced to a minimum value that is acceptable for all
 samples. For example, if all sample values in a 16-bit image have the form
 (256+1)*n, (e.g. #0000, #2323, #FFFF), then the bit depth can be
 reduced to 8, and the new sample values will become n, (e.g. #00, #23,
 #FF).
</li>
<li>
 Color type reduction
 
 - If an RGB image has 256 distinct colors or less, it can be reencoded as a
 Palette image.
 
 - If an RGB or Palette image has only gray pixels, it can be reencoded as
 Grayscale.
 
 A color type reduction can also enable a bit depth reduction.
</li>
<li>
 Color palette reduction
 
 If the color palette contains redundant entries (i.e. duplicate entries that
 indicate the same RGB value) or sterile entries (i.e. entries that do not
 have a correspondent in the raw pixel data), these entries can be removed.
 
 A color palette reduction can also enable a bit depth reduction.
</li>
<li>
 Alpha channel reduction
 
 If all pixels in a Grayscale+Alpha or an RGB+Alpha image are fully opaque
 (i.e. all alpha components are equal to 2^bitdepth-1), or if the
 transparency information can be stored entirely in a (much cheaper)
 <code>tRNS</code> chunk, the alpha channel can be stripped.
</li>
</ul>

There are, however, a few cases when some image type reductions do not
necessarily lead to the reduction of the compressed stream. The
<a href="http://www.cs.toronto.edu/~cosmin/pngtech/">PNG-Tech</a> site contains
experimental analyses of these possibilities; for example, see the article
<a href="8bpp.html">8 bits per pixel in paletted images</a>.

Interlacing, useful for a faster, progressive rendering, is another component
of the PNG image type that affects compression. In an interlaced stream, the
samples corresponding to neighboring pixels are stored far away, hence the data
in it is less correlated and less compressible. Unlike JPEG, where interlacing
may improve the compression slightly, the PNG interlacing degrades the
compression significantly.

<h4>2.2 The PNG delta filters</h4>

The role of filtering can be illustrated in the following example. Assume the
sequence 2, 3, 4, 5, 6, 7, 8, 9. Although it has much redundancy, the sequence
is not compressible by a Ziv-Lempel compressor, nor by a Huffman compressor.
However, if one makes a simple and reversible transformation, replacing each
value with the numerical difference between it and the value to its left, the
sequence becomes 2, 1, 1, 1, 1, 1, 1, 1, which is highly compressible.

The PNG format employs five types of filters: None,
Left, Up, Average, and
Paeth. The first filter leaves the original data intact, and the
other four are subtracting from each pixel a value that involves the
neighbor pixels from the left, up, and/or the upper left.

A certain filter is assigned to each row, and is applied to all pixels from
that row. Therefore, an image can be delta-filtered in a huge number of
possible configurations (5 ^ height), and each configuration
leads to a different compressed output. Two different filter configurations may
make a difference in the compressed file size by a couple of factors, so a
careful choice of filters is of paramount importance.

It is possible to apply a single filter to all rows, or to apply different
filters to different rows. In the former case, the filtering process is
fixed; in the latter, it is adaptive.

<div>
While an exhaustive search is unfeasible, the PNG Specification suggests a
heuristic filtering strategy:
</div>
<ul>
<li>
 If the image type is Palette, or the bit depth is smaller than 8, then
 do not filter the image (i.e. use fixed filtering, with the filter
 None).
</li>
<li>
 (The other case) If the image type is Grayscale or RGB (with or without
 Alpha), and the bit depth is not smaller than 8, then use adaptive filtering
 as follows: independently for each row, apply all five filters and
 select the filter that produces the smallest sum of absolute values per row.
</li>
</ul>

Cases where the above heuristics are less than optimal are shown on the
<a href="http://www.cs.toronto.edu/~cosmin/pngtech/">PNG-Tech</a>
site; for example, see
<a href="better-filtering.html">Brute-force vs. heuristic filtering</a>.

<h4>2.3 The strategy of searching LZ77 matches</h4>

The Ziv-Lempel algorithm works under the assumption that contiguous sequences
appear repeatedly in the input stream. If the sequence to be encoded matches
one or more sequences already present in the sliding history window, the
encoder sends a LZ77 pair (distance, length) that points to the
closest match. In most LZ77 incarnations, including Deflate, smaller
distance codes are encoded more concisely.

In Deflate, in particular, the regular (non-matched) symbols, and the match
lengths, are sent to the same Huffman coder, while the match distances are sent
to a separate Huffman coder. If the LZ77 matches fall between the accepted
boundaries (i.e. they are not shorter than 3 and not longer than 258), a greedy
strategy will accept them as a replacement for the symbols to which they
correspond.

The greedy strategy is preferable when compressing text files, or many types of
binary files, but it may be suboptimal when compressing filtered data, such as
the byte strings that come from a PNG filter. Filtered data consist mostly of
small values with a pseudo-random distribution. Therefore, in certain
situations, it may be desirable to favor the encoding of individual symbols,
even if matches that may replace these symbols exist.

The
<a href="http://www.zlib.org/">zlib Reference Library</a>
is a reference implementation of Deflate, which is further used by the
<a href="http://www.libpng.org/pub/png/libpng.html">PNG Reference Library</a>.
By default, zlib selects the greedy strategy, but the user is able to
specify his or her custom preference via the <code>strategy</code> parameter.
This parameter can take one of the following values:
 
- <code>Z_DEFAULT_STRATEGY = 0</code>, the default greedy search strategy.
 
- <code>Z_FILTERED = 1</code>, a strategy in which the matches are accepted
only if their length is 6 or bigger.
 
- <code>Z_HUFFMAN_ONLY = 2</code>, a fast strategy in which the Ziv-Lempel
algorithm is entirely bypassed, and all the symbols from the input are encoded
directly by the Huffman coder.
 
- <code>Z_RLE = 3</code> (appeared in the zlib-1.2.x series), a fast
strategy in which the LZ77 algorithm is essentially reduced to the Run-Length
Encoding algorithm. In other words, the matches are accepted only if their
distance is 1. For example, the 10-symbol sequence "<code>aaaaaaaaaa</code>"
can be LZ77-encoded as
['<code>a</code>', (distance=1, length=9)];
by removing distance=1 from the picture, this encoding can be regarded
as a peculiar run-length encoding (which differs from the classic RLE by using
length=9 instead of count=10).
 
The <code>strategy</code> parameter affects only the compression ratio. It does
not affect the correctness of the compressed output, even if it is set to an
inappropriate value.

It was experimentally observed that the LZ77 search is occasionally capable of
producing smaller PNGs if it is less exhaustive. The reason behind this act
resides in the same category of "strategic searches" discussed here.
Unfortunately, there is no known method of anticipating which search level
(from the fastest and the least exhaustive, to the slowest and the most
exhaustive) is better, other than assuming "the most exhaustive is better in
most cases".

Unfortunately, even a "filtered" strategy does not always produce better
results than a "greedy" strategy on filtered input, and the only known method
to obtain the best combination is by multiple trials. Experiments and
measurements can, again, be found on the
<a href="http://www.cs.toronto.edu/~cosmin/pngtech/">PNG-Tech</a>
site; for example, see the original
<a href="z_rle.html">Z_RLE strategy proposal</a>.

<h4>2.4 The size of Huffman buffers</h4>

As mentioned earlier, the entropy encoder inside the Deflate method is the
static Huffman algorithm. The output of LZ77 is fed into a buffer which is
occasionally flushed by sending a static Huffman tree followed by all the
Huffman codes, to the output of Deflate. After this, both the buffer and the
Huffman tree are reset, waiting for the subsequent LZ77 codes to come and
refill the buffer.

<blockquote>
The Deflate specification refers to dynamic Huffman codes. However, this
is a misnomer, in which the term dynamic is used in contrast to the
fixed Huffman codes. The fixed Huffman codes are simply built according
to a predefined Huffman tree, without regard to the actual symbol frequencies.
The dynamic Huffman codes referred to by the Deflate specification are NOT
built by the dynamic Huffman algorithm, as defined, for example, by Faller,
Gallager and Knuth (the FGK algorithm), or by Vitter (the V algorithm).
The predefined Huffman tree was introduced in PKZIP as a fast
compression alternative, but it produces poor results even on text, and it is
almost useless in PNG compression. Still, a PNG stream that contains codes
built by the fixed (predefined) Huffman tree, is a valid stream, and a
compliant PNG reader must decode this stream correctly.
</blockquote>

It is desirable to establish the buffer boundaries so that sequences conforming
to the same probability model are fit in the same Huffman buffer. Methods for
approaching these boundaries exist, but they are not used in the mainstream
Deflate implementation(s). Instead, the buffers are flushed when a limit
(typically, 16k LZ77 codes) is reached. This is, however, a fast approach, and
the results are satisfactory.

The size of Huffman buffers is indirectly determined by the encoder's memory
(usage) level. For this reason, certain memory levels might be good for certain
types of images.

<h3>3. PNG (lossless) optimization programs</h3>

The multitude of PNG encoding programs is listed at
<a href="http://www.libpng.org/pub/png/pngapps.html">http://www.libpng.org/pub/png/pngapps.html</a>.
Their performance varies as much as the range of possible compression ratios;
the good encoders are at least applying the filtering heuristics, described
briefly in the PNG Specification, and illustrated above.
 
Some programs gain extra compression by discarding some of the data in the
input images (so these programs are lossy!)

This section contains the small list of PNG optimization programs
that show a particular concern towards obtaining a file size as small as
possible. They work by performing repeated compression trials, applying various
parameter sets, and selecting the parameter set that yields the smallest
compressed output.

<ul>
<li>
 
 pngrewrite by Jason Summers, available at
 <a href="http://www.pobox.com/~jason1/pngrewrite/">http://www.pobox.com/~jason1/pngrewrite</a>,
 is an open-source program that performs lossless image reductions.
 It works best in conjunction with pngcrush (see below); the user
 should run pngcrush after pngrewrite.
 
</li>
<li>
 
 pngcrush by Glenn Randers-Pehrson, available at
 <a href="http://pmt.sourceforge.net/pngcrush/">http://pmt.sourceforge.net/pngcrush</a>,
 is an open-source program that iterates over PNG filters and zlib (Deflate)
 parameters, compresses the image repeatedly using each parameter
 configuration, and chooses the configuration that yields the smallest
 compressed (IDAT) output.
 At the user's option, the program can explore few (below 10) or many (a
 brute-force traversal over more than 100) configurations. The method of
 selecting the parameters for "few" trials is particularly effective, and the
 use of a brute-force traversal is generally not recommended.
 
 
 In addition, pngcrush offers a multitude of extra features, such as
 recovery of erroneous PNG files (e.g. files containing bad CRCs), and
 chunk-level editing of PNG meta-data.
 
</li>
<li>
 
 OptiPNG by Cosmin Truţa, available at
 <a href="http://www.cs.toronto.edu/~cosmin/pngtech/optipng/">http://www.cs.toronto.edu/pngtech/optipng</a>,
 is a newer open-source program, inspired from pngcrush, but designed
 to be more flexible and to run faster.
 Unlike pngcrush, OptiPNG performs the trials entirely in
 memory, and writes only the final output file on the disk. Moreover, it
 offers multiple optimization presets to the user, who can choose among a
 range of options from "very few trials" to "very many trials" (in contrast to
 the coarser "smart vs. brute" option offered by pngcrush).
 
 
 It is important to mention that the achieved compression ratio is less and
 less likely to improve when higher-level presets (trigerring more trials)
 are being used. Even if the program is capable of searching automatically
 over more than 200 configurations (and the advanced users have access to more
 than 1000 configurations!), a preset that selects around 10 trials should be
 satisfactory for most users. Furthermore, a preset that selects between
 30-40 trials should be satisfactory for all users, for it is very,
 very unlikely to be beaten significantly by any wider search. The rest of the
 trial configurations are offered rather as a curiosity (but they were used in
 the experimentation from which we concluded they are indeed useless!)
 
</li>
<li>
 
 AdvanceCOMP by Andrea Mazzoleni is a set of tools for optimizing
 ZIP/GZIP, PNG and MNG files, based on the powerful 7-Zip deflation
 engine. The name of the PNG optimization tool is AdvPNG. At the time
 of this writing, AdvPNG does not perform image reductions, so the use
 of pngrewrite or OptiPNG prior to optimiziation may be
 necessary. However, given the effectivenes of 7-Zip deflation,
 AdvanceCOMP is a powerful contender.
 
 
 The AdvanceCOMP tool set is a part of the AdvanceMAME project,
 available at
 <a href="http://advancemame.sourceforge.net/">http://advancemame.sourceforge.net</a>.
 
</li>
<li>
 
 PNGOut by Ken Silverman, available at
 <a href="http://advsys.net/ken/utils.htm">http://advsys.net/ken/utils.htm</a>,
 is a freely-available compiled program (no source code), running on
 Windows and Linux. According to our tests, the compression ratio achieved by
 PNGOut is comparable to that of AdvPNG.
 Unfortunately, due to the lack of information, we cannot say much about this
 tool.
 
 
 A nice GUI frontend for PNGOut, named PNGGauntlet, is
 available at
 <a href="http://www.numbera.com/software/pnggauntlet.aspx">http://www.numbera.com/software/pnggauntlet.aspx</a>.
 
</li>
</ul>

<h3>4. An extra note on losslessness</h3>

What is lossless PNG optimization, after all? This is a straightforward
question, whose answer is intuitive, yet not so straightforward.

Losslessness in the strictest sense, where no information whatsoever is lost,
can only be achieved by leaving the original file (any file) intact, or
by transforming it (e.g. compressing it, encrypting it) in such a way that
there is an inverse transformation which recovers it completely, bit by bit.

In the case of PNG images, this condition of strict losslessness has little
relevance to the casual graphics user, and is, therefore, too strong.
There are instances where strict losslessness is required; for example, when
handling certified PNG files whose integrity is guaranteed by an external
checksum like MD5 or SHA, or by a digital signature such as
<code>dSIG</code>. Most of the time, however, it is desirable to relax
the notion of PNG losslessness, to the extent of not losing any information
that pertains to the rendered image and to the
semantic value of the metadata that accompanies the image. This allows
the user to concentrate on what is really important when it comes to preserving
the contents of a PNG image, and enables the concept of PNG optimization tools.

<blockquote>
A lossless transform of a PNG image file is a transform which
fully preserves the rendered RGB triples (the RGB triples that come
either directly, or from a palette index, or from a gray->RGB expansion), the
rendered transparency (the alpha samples that come either directly, or
from a <code>tRNS</code> chunk, or the implicit 100% opacity assumed due
to the lack of any explicit transparency information), the order of
rendering (sequential or interlaced), and the semantics contained by the
ancillary chunks.
</blockquote>

<div>
This definition allows the execution of the above-mentioned image reduction
operations, and the recompression of <code>IDAT</code>. It also allows
the alteration or the elimination of other pieces of information that are
technically valid, but have no influence on any presentation of the image
pixels:
</div>
<ul>
<li>
 The information that pertains to Deflate streams, either inside
 <code>IDAT</code>, or in other compressed chunks like
 <code>zTXt</code>, <code>iTXt</code> or
 <code>iCCP</code>; e.g. the LZ77 window size, the type and size of
 Deflate blocks, etc. (The only thing that matters is that the
 decompressed byte sequence must remain the same.)
</li>
<li>
 The order of palette entries inside a <code>PLTE</code> chunk. (When
 changing this order, the information that depends on it, such as the
 palette-encoded pixels or the <code>tRNS</code> information, must be
 updated accordingly.)
</li>
<li>
 RGB triples that do not correspond to any pixel in the actual image, but are
 stored in a <code>tRNS</code> chunk.
</li>
<li>
 Fully opaque <code>tRNS</code> entries in a palette image.
</li>
<li>
 Gamma correction (<code>gAMA</code>) or significant bit
 (<code>sBIT</code>) information inside an image that consists
 exclusively of samples whose intensity is either minimum (0) or maximum
 (2^bitdepth-1).
</li>
<li>
 The fact that a textual comment is stored uncompressed in a
 <code>tEXt</code> chunk, or compressed in a <code>zTXt</code>
 chunk, or with no translation in an <code>iTXt</code> chunk.
</li>
<li>
 Etcetera.
</li>
</ul>

If any of the discardable information is important in a particular application,
and lossless PNG optimization is still desirable, it is recommended to store
this information in ancillary chunks, rather than hack it inside critical
chunks. For example, if sterile palette entries are necessary (e.g. for later
editing stages), it is recommended to store them inside a suggested palette
(<code>sPLT</code>) chunk, rather than keeping them inside
<code>PLTE</code>.

<h3>5. Selective bibliography</h3>

Besides the discussed specifications, the references below provide essential
information necessary to comprehend the contents of this article.

<ul>
<li>
 Thomas Boutell, Glenn Randers-Pehrson et al.
 Portable Network Graphics (PNG) Specification, Second Edition.
 ISO/IEC 15948:2003(E); W3C Recommendation 10 November 2003.
</li>
<li>
 David A. Huffman.
 A method for the construction of minimum redundancy codes.
 In Proceedings of the Institute of Radio Engineers,
 vol. 40, no. 9, pp. 1098-1101, September 1952.
</li>
<li>
 Jacob Ziv and Abraham Lempel.
 A universal algorithm for data compression.
 IEEE Transactions on Information Theory,
 vol. IT-23, no. 3, pp. 337-343, May 1977.
 
 
 Due to a historical accident, the famous algorithm is better-known as the
 "Lempel-Ziv (LZ) algorithm", even though the "Ziv-Lempel algorithm" is a
 more legitimate name.
 
</li>
<li>
 Greg Roelofs.
 PNG: The definitive guide.
 O'Reilly and Associates, 1999.
</li>
</ul>

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