This JavaScript image compressor uses the Browser's native canvas.toBlob API to do the compression work, which means it is lossy compression, asynchronous, and has different compression effects in different browsers. You can use it to precompress an image on the client side before uploading it.
The objective of image compression is to reduce irrelevance and redundancy of the image data to be able to store or transmit data in an efficient form. It is concerned with minimizing the number of bits required to represent an image. Image compression may be lossy or lossless. Lossless compression is preferred for archival purposes and often for medical imaging, technical drawings, clip art, or comics. Lossy compression methods, especially when used at low bit rates, introduce compression artifacts.
Lossy methods are especially suitable for natural images such as photographs in applications in which minor (sometimes imperceptible) loss of fidelity is acceptable to achieve a substantial reduction in bit rate. The lossy compression that produces imperceptible differences may be called visually lossless. We will provide an overview of image compression methods in Section 1.3.
Besides these three techniques, in fact, we can view the process of information embedding in cover images as a special kind of digital image processing, since both its input and output are digital images.
While image compression is a well developed field, progress is continuing due to the demand for higher compression performance from an increasing number of application areas. For example, JPEG-2000 [20] is an emerging standard for still image compression, and MPEG [11] is a standard for coding moving pictures. For remote time-varying visualization tasks, low cost is probably the most relevant selection criterion; low decompression cost is particularly important when considering our remote visualization setting because computing resources are generally low at the receiving end. This eliminates JPEG-2000 (based on wavelet transform) because of its relatively high computational complexity, even though it provides significantly lower distortion for the same bit rate. JPEG-2000 also requires more memory than JPEG.
Rendering time-varying data produces an animation sequence. MPEG, which is good for compressing existing videos, is not well suited for our interactive setting, in which each image is generated on the fly and is displayed in real time. Using MPEG is not completely impossible, but the overhead would be too high to make both the encoding and the decoding efficient in software.
Therefore, we consider three other more favorable compression methods: LZO, BZIP, and JPEG. LZO does lossless data compression, and it offers fast compression and very fast decompression. The decompression requires no extra memory. In addition, there are slower compression levels that achieve a quite competitive compression ratio while still decompressing at very high speed. In summary, LZO is well suitable for data compression or decompression in real time, which means it favors speed over compression ratio.
BZIP has very good lossless compression; it is better than gzip in compression and decompression time. BZIP compresses data using the Burrows-Wheeler block-sorting compression algorithm [3] and Huffman coding. Its compression is generally considerably better than that achieved by more conventional LZ77/LZ78-based compressors, and it approaches the performance of the PPM family of statistical compressors.
JPEG is designed to compress full-color images of real-world scenes by exploiting known limitations of the human eye, notably the fact that small color changes are perceived less accurately than small changes in brightness. The most widely implemented JPEG subset is the “baseline” JPEG, which provides lossy compression, though the user can control the degree of loss by adjusting certain parameters. Another important aspect of JPEG is that the decoder can also trade off decoding speed against image quality by using fast but inaccurate approximations of the required calculations. Remarkable speedups for decompression can be achieved in this way.
Consequently, JPEG provides the flexibility to cope with the required frame rates. The newer “lossless JPEG,” JPEG-LS, offers mathematically lossless compression. The decompressed output of the “baseline JPEG” can be visually indistinguishable from the original image. JPEG-LS gives better compression than original JPEG, but still nowhere near what one can get with a lossy method. Further discussion of compression methods can be found in many published reports and websites and is beyond the scope of this chapter.
How to Resize Images Online
Resizing images is an important part of working with digital media, whether you are uploading images to your website, sharing them on social media, or sending them in an email. Fortunately, there are many online tools available that can help you resize your images quickly and easily. In this article, we will go over how to resize images online using some of these tools.
Step 1: Choose an Online Image Resizing Tool
There are many online image resizing tools available, and choosing the right one can depend on your specific needs. Some popular options include:
- Pixlr: a powerful online photo editor that includes a range of resizing options
- ResizePixel: a simple tool that allows you to resize images by entering specific dimensions or by percentage
- Canva: a graphic design tool that includes image resizing options as well as a range of other design features
Step 2: Upload Your Image
Once you have chosen an online image resizing tool, you will need to upload the image you want to resize. Most tools will allow you to upload images from your computer or from a URL.
Step 3: Choose Your Resize Settings
After you have uploaded your image, you will need to choose your resize settings. This can include choosing the new dimensions of your image, whether to keep the aspect ratio, and selecting the file format.
Step 4: Preview and Save Your Resized Image
Before you save your resized image, it is a good idea to preview it to make sure it looks the way you want it to. Once you are satisfied with the resized image, you can save it to your computer or share it directly from the online tool.
Tips for Resizing Images Online
Here are some tips to keep in mind when resizing images online:
- Always make sure to keep a backup copy of your original image in case you need to revert to it later
- Keep in mind that resizing an image can affect its quality,
It's important to find the right balance between size and quality. If you need to maintain high image quality, consider using a tool that includes advanced image compression features.
- Consider the aspect ratio of your image when resizing. If you want to maintain the original aspect ratio, make sure to choose a resizing option that allows you to lock the aspect ratio.
- When choosing a file format for your resized image, consider the purpose of the image. For example, JPEG is a good option for photographs, while PNG is better suited for images with transparency or sharp edges.
Resizing images online is a simple and efficient way to adjust the size of your digital media. By choosing an online image resizing tool, uploading your image, choosing your resize settings, and previewing and saving your resized image, you can quickly and easily adjust your images to fit your needs. Just remember to keep a backup of your original image and consider the aspect ratio and file format when resizing to ensure the best results.