WebP
WebP is a modern image format that provides superior lossless and lossy compression for images on the web. Using WebP, webmasters and web developers can create smaller, richer images that make the web faster.
WebP lossless images are 26% smaller in size compared to PNGs. WebP lossy images are 25-34% smaller than comparable JPEG images at equivalent SSIM quality index.
Lossless WebP supports transparency (also known as alpha channel) at a cost of just 22% additional bytes. For cases when lossy RGB compression is acceptable, lossy WebP also supports transparency, typically providing 3× smaller file sizes compared to PNG.
Compression Techniques
At Google, we are constantly looking at ways to make web pages load faster. One way to do this is by making web images smaller. Images comprise up to 60%-65% of bytes on most web pages and page size is a major factor in total rendering time. Page size is especially important for mobile devices, where smaller images save both bandwidth and battery life.
WebP is a new image format developed by Google and supported in Chrome, Opera and Android that is optimized to enable faster and smaller images on the Web. WebP images are about 30% smaller in size compared to PNG and JPEG images at equivalent visual quality. In addition, the WebP image format has feature parity with other formats as well. It supports:
- Lossy compression: The lossy compression is based on VP8 key frame encoding. VP8 is a video compression format created by On2 Technologies as a successor to the VP6 and VP7 formats.
- Lossless compression: The lossless compression format is developed by the WebP team.
- Transparency: 8-bit alpha channel is useful for graphical images. The Alpha channel can be used along with lossy RGB, a feature that’s currently not available with any other format.
- Animation: It supports true-color animated images.
- Metadata: It may have EXIF and XMP metadata (used by cameras, for example).
- Color Profile: It may have an embedded ICC profile.
Due to better compression of images and support for all these features, WebP can be an excellent replacement for most image formats: PNG, JPEG or GIF. Even better, did you know that WebP enables new image optimization opportunities, such as support for lossy images with transparency? Yep! WebP is the Swiss Army knife of image formats.
So, how is this magic done? Let’s roll up our sleeves and take a look under the hood.
Lossy WebP
WebP’s lossy compression uses the same methodology as VP8 for predicting (video) frames. VP8 is based on block prediction and like any block-based codec, VP8 divides the frame into smaller segments called macroblocks.
Within each macroblock, the encoder can predict redundant motion and color information based on previously processed blocks. The image frame is “key” in the sense that it only uses the pixels already decoded in the immediate spatial neighborhood of each of the macroblocks, and tries to inpaint the unknown part of them. This is called predictive coding (see intra-frame coding of the VP8 video).
The redundant data can then be subtracted from the block, which results in more efficient compression. We are only left with a small difference, called residual, to transmit in a compressed form.
After being subject to a mathematically invertible transform (the famed DCT, which stands for Discrete Cosine Transform), the residuals typically contain many zero values, which can be compressed much more effectively. The result is then quantized and entropy-coded. Amusingly, the quantization step is the only one where bits are lossy-ly discarded (search for the divide by QPj in the diagram below). All other steps are invertible and lossless!
The following diagram shows the steps involved in WebP lossy compression. The differentiating features compared to JPEG are circled in red.
WebP uses block quantization and distributes bits adaptively across different image segments: fewer bits for low entropy segments and higher bits for higher entropy segments. WebP uses Arithmetic entropy encoding, achieving better compression compared to the Huffman encoding used in JPEG.
VP8 Intra-prediction Modes
VP8 intra-prediction modes are used with three types of macroblocks:
- 4×4 luma
- 16×16 luma
- 8×8 chroma
Four common intra-prediction modes are shared by these macroblocks:
H_PRED(horizontal prediction). Fills each column of the block with a copy of the left column, L.V_PRED(vertical prediction). Fills each row of the block with a copy of the above row, A.DC_PRED(DC prediction). Fills the block with a single value using the average of the pixels in the row above A and the column to the left of L.TM_PRED(TrueMotion prediction). A mode that gets its name from a compression technique developed by On2 Technologies. In addition to the row A and column L, TM_PRED uses the pixel P above and to the left of the block. Horizontal differences between pixels in A (starting from P) are propagated using the pixels from L to start each row.
For 4×4 luma blocks, there are six additional intra modes similar to V_PRED and H_PRED, but that correspond to predicting pixels in different directions. More detail on these modes can be found in the VP8 Bitstream Guide.
Adaptive Block Quantization
To improve the quality of an image, the image is segmented into areas that have visibly similar features. For each of these segments, the compression parameters (quantization steps, filtering strength, etc.) are tuned independently. This yields efficient compression by redistributing bits to where they are most useful. VP8 allows a maximum of four segments (a limitation of the VP8 bitstream).
Why WebP (lossy) is Better than JPEG
Prediction coding is a main reason WebP wins over JPEG. Block adaptive quantization makes a big difference, too. Filtering helps at mid/low bitrates. Boolean arithmetic encoding provides 5%-10% compression gains compared to Huffman encoding.
Lossless WebP
The WebP-lossless encoding is based on transforming the image using several different techniques. Then, entropy coding is performed on the transform parameters and transformed image data. The transforms applied to the image include spatial prediction of pixels, color space transform, using locally emerging palettes, packing multiple pixels into one pixel, and alpha replacement. For the entropy coding we use a variant of LZ77-Huffman coding, which uses 2D encoding of distance values and compact sparse values.
Predictor (Spatial) Transform
Spatial prediction is used to reduce entropy by exploiting the fact that neighboring pixels are often correlated. In the predictor transform, the current pixel value is predicted from the pixels that are already decoded (in scan-line order), and only the residual value (actual – predicted) is encoded. The prediction mode determines the type of prediction to use. The image is divided into multiple square regions and all the pixels in one square use the same prediction mode.
There are 13 different possible prediction modes. Prevalent ones are left, top, top-left & top-right pixels. The remaining ones are combinations (averages) of left, top, top-left and top-right.
Color (de-correlation) Transform
The goal of the color transform is to decorrelate the R, G and B values of each pixel. Color transform keeps the green (G) value as it is, transforms red (R) based on green, and transforms blue (B) based on green and then based on red.
As is the case for the predictor transform, first the image is divided into blocks and the same transform mode is used for all the pixels in a block. For each block there are three types of color transform elements: green_to_red, green_to_blue, and red_to_blue.
Subtract Green Transform
The “subtract green transform” subtracts the green values from the red and blue values of each pixel. When this transform is present, the decoder needs to add the green value to both red and blue. This is a special case of the general color decorrelation transform above, frequent enough to warrant a cutoff.
Color Indexing (palettes) Transform
If there are not many unique pixel values, it may be more efficient to create a color index array and replace the pixel values by the array’s indices. The color indexing transform achieves this. The color indexing transform checks for the number of unique ARGB values in the image. If that number is below a threshold (256), it creates an array of those ARGB values, which is then used to replace the pixel values with the corresponding index.
Color Cache Coding
Lossless WebP compression uses already-seen image fragments in order to reconstruct new pixels. It can also use a local palette if no interesting match is found. This palette is continuously updated to reuse recent colors. In the picture below, you can see the local color cache in action being updated progressively with the 32 recently-used colors as the scan goes downward.
LZ77 Backward Reference
Backward references are tuples of length and distance code. Length indicates how many pixels in scan-line order are to be copied. Distance code is a number indicating the position of a previously seen pixel, from which the pixels are to be copied. The length and distance values are stored using LZ77 prefix coding.
LZ77 prefix coding divides large integer values into two parts: the prefix code and the extra bits. The prefix code is stored using an entropy code, while the extra bits are stored as they are (without an entropy code).
The diagram below illustrates the LZ77 (2D variant) with word-matching (instead of pixels).
Lossy WebP with Alpha
In addition to lossy WebP (RGB colors) and lossless WebP (lossless RGB with alpha), there’s another WebP mode that allows lossy encoding for RGB channels and lossless encoding for the alpha channel. Such a possibility (lossy RGB and lossless alpha) is not available today with any of the existing image formats. Today, webmasters who need transparency must encode images losslessly in PNG, leading to a significant size bloat. WebP alpha encodes images with low bits- per-pixel and provides an effective way to reduce the size of such images. Lossless compression of the alpha channel adds just 22% bytes over lossy (quality 90) WebP encoding.
Overall, replacing transparent PNG with lossy+alpha WebP gives 60-70% size saving on average. This has been confirmed as a great attracting feature for icon-rich mobile sites (everything.me, for example).
How WebP Works
Lossy WebP compression uses predictive coding to encode an image, the same method used by the VP8 video codec to compress keyframes in videos. Predictive coding uses the values in neighboring blocks of pixels to predict the values in a block, and then encodes only the difference.
Lossless WebP compression uses already seen image fragments in order to exactly reconstruct new pixels. It can also use a local palette if no interesting match is found.
A WebP file consists of VP8 or VP8L image data, and a container based on RIFF. The standalone libwebp library serves as a reference implementation for the WebP specification, and is available from our Git repository or as a tarball.
WebP Support
WebP is natively supported in Google Chrome, Firefox, Edge, the Opera browser, and by many other tools and software libraries. Developers have also added support to a variety of image editing tools.
WebP includes the lightweight encoding and decoding library libwebp and the command line tools cwebp and dwebp for converting images to and from the WebP format, as well as tools for viewing, muxing and animating WebP images. The full source code is available on the download page.