image compression using html css js .pptx

image compression using html css js .pptx

• 1.
  Image Compression
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  Introduction to ImageCompression  Definition :- Image compression is the process of reducing the size of an image file without significantly degrading its quality.  Why compress images? I. To save storage space II. To reduce bandwidth for faster transmission III. To optimize web performance and mobile apps IV. To reduce cost for cloud storage and data usage
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  History of ImageCompression  1960s–1970s: Early Techniques :- I. Simple methods like Run-Length Encoding (RLE) and Predictive Coding. II. Focus on reducing data redundancy in black-and-white images.  1996: PNG Format :- I. A lossless compression format. II. Replaced GIF; supports transparency. III. Uses the Deflate algorithm.
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  History of ImageCompression  JPEG 2000 I. Based on Wavelet Transform (DWT). II. Better quality, but less adopted due to complexity.  2010s: New Web Formats I. WebP (Google) – efficient for web, supports both lossless/lossy. II. AVIF – modern open-source format with high efficiency.  2020s: AI-Based Compression I. Use of deep learning for smarter, perceptual-based compression.
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  Need for ImageCompression  Faster transmission :- Compressed images load quicker on web/mobile.  Images take a lot of space :- Uncompressed images like BMP or TIFF can be very large.  Limited bandwidth environments :- Required in streaming and mobile networks.  Archiving :- Large image datasets (e.g., medical imaging) need efficient storage.
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  Types of ImageCompression  Lossless Compression :- 1. No loss of data or image quality 2. Image can be fully reconstructed 3. Suitable for critical applications (e.g., medical imaging)  Lossy Compression :- 1. Some data is lost, can't be recovered 2. Much smaller file sizes 3. Acceptable for everyday images (e.g., web photos)
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  Lossless Compression Techniques Run-Length Encoding (RLE) :- 1. Stores repeating values as (value, count) 2. Good for images with large areas of same color  Huffman Coding :- 1. Replaces common patterns with shorter codes 2. Used in PNG and JPEG formats  LZW (Lempel–Ziv–Welch) :- 1. Dictionary-based 2. Used in GIF, TIFF, and PNG
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  Lossy Compression Techniques Color Space Transformation :- 1. Convert RGB to YCbCr 2. Reduces data for chrominance channels  Discrete Cosine Transform (DCT) :- 1. Converts image to frequency domain 2. High-frequency data (less visible) is discarded  Quantization :- 1. Approximate similar values 2. Main reason for data loss in JPEG
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  Process of ImageCompression
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  Applications  Web Development:- Fast-loading websites, SEO.  Medical Imaging :- Efficient transmission of large scan files.  Satellite Imaging :- Store and send high-res photos from space.  Mobile Devices :-Save space and battery life.  Multimedia :- Image storage in videos, animations.
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  Advantages & Disadvantages Advantages :- 1. Saves disk space 2. Faster image transfer and loading 3. Lowers costs for cloud and web hosting  Disadvantages :- 1. Lossy methods reduce image quality 2. Some compression artifacts may appear (blurring, blocking) 3. Cannot fully recover original image from lossy formats
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  Future Scope  AI& Machine Learning Integration :-  Deep learning models (CNNs, GANs) will optimize image compression based on content and human perception.  Real-Time Compression :-  Required for video conferencing, live streaming, and augmented reality (AR).  Web & Cloud Optimization :-  Demand for faster website loading and lower bandwidth usage is rising.  Secure & Privacy-Aware Compression :-  Emerging need to compress without compromising sensitive data.
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  Image Compression Tools& Libraries  Online Tools :- i. TinyPNG, Compressor.io, JPEG-Optimizer  Programming Libraries :- i. Python: Pillow, OpenCV, PyTorch (for AI compression) ii. JavaScript: Compressor.js, browser APIs iii. C/C++: libjpeg, libpng
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  Conclusion  Image compressionis essential for modern computing  Choice between lossless and lossy depends on use case  Understanding algorithms helps in selecting right format  Future trends are heading towards AI-powered techniques
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  References  Sukhwinder Singh,Vinod Kumar, H.K.Verma, Adaptive threshold based block classification in medical image compression.  SalehaMasood, Muhammad Sharif, MussaratYasmin, MudassarRaza and SajjadMohsin. Brain Image Compression.  Ajit Singh, MeenakshiGahlawat, " Image Compression and its Various Techniques.
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  References  S. Kumar,T. U. Paul, A. Raychoudhury, “Image Compression using Approximate Matching and Run Length”, International Journal of Advanced Computer Science and Applications.  S. Kumar, T. U. Paul, A. Raychoudhury, “Image Compression using Approximate Matching and Run Length”, International Journal of Advanced Computer Science and Applications, Vol. 2, No. 6, 2011.