To compress all output files in a ZIP file, click "" icon on the right, then click "Add to ZIP". To download one single file, simply right-click on file link and click "Save link as...".
|File Name||Original Size||Output Size||Size Compressed||Actions|
Set WEBP quality first. WEBP quality value can be 1 (lowest image quality and highest compression) to 100 (best quality but least effective compression). Please note it only works on non-animated WEBP images. The settings are optional, you can close "Settings" section by clicking the "X" on the right.
You can drag multiple WEBP files to the "Add Files" section. Each file can be up to 40m.
The batch compression automatically starts when files are uploaded. Please be patient while files are uploading or compressing.
The output files will be listed in the "Conversion Results" section. To compress all output files in a ZIP file, click "" icon on the right, then click "Add to ZIP". You can right-click on file name and click "Save link as..." to save the file. The output files will be automatically deleted on our server in two hours, so please download it to your computer or save it to online storage services such as Google Drive or Dropbox as soon as possible.
WebP is an image format employing both lossy and lossless compression. It is currently developed by Google, based on technology acquired with the purchase of On2 Technologies.
WebP's lossy compression algorithm is based on the intra-frame coding of the VP8 video format and the Resource Interchange File Format (RIFF) as a container format. As such, it is a block-based transformation scheme with eight bits of color depth and a luminance-chrominance model with chroma subsampling by a ratio of 1:2 (YCbCr 4:2:0). Without further content, the mandatory RIFF container has an overhead of only twenty bytes, though it can also hold additional metadata. The side length of WebP images is limited to 16383 pixels.
WebP’s lossless compression uses advanced techniques such as dedicated entropy codes for different color channels, exploiting 2D locality of backward reference distances and a color cache of recently used colors. This complements basic techniques such as dictionary coding, Huffman coding and color indexing transform.