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JPEG2000: Still Image Compression

JPEG2000: Still Image Compression. Shyh-Jye Ni Shoaib Junaid. Introduction.

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JPEG2000: Still Image Compression

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  1. JPEG2000: Still Image Compression Shyh-Jye Ni Shoaib Junaid

  2. Introduction • Over the past few years, digital imaging has gone from a high-end niche capability to a ubiquitous mainstream application, with the widespread use of digital pictures greatly enriching the content of both commercial and personal communications. With millions of companies and consumers using digital images in both print and on-line environments, the need has emerged for developing a new, comprehensive, flexible and universally deployable file format for digital images.

  3. Motivation • Improved Compression Efficiency • Richer Content and Capacity for Alternate Color Spaces • Support for Flexible "Level of Interest" Access • Conservation of Bandwidth

  4. Compressed Image Data Original Image Data Pre-Processing Discrete Wavelet Transform (DWT) Uniform Quantizer with Deadzone Tier-1 Coding Tier-2 Coding JPEG 2000 fundamental building blocks Overview • Pre-Process: • Partition the input image into rectangular and non-overlapping tiles of equal size (except possibly for those tiles at the image borders). The tile size is arbitrary and can be as large as the original image itself or as small as a single pixel.  • Next, unsigned sample values in each component are level shifted (DC offset) by subtracting a fixed value of 2B_1 from each sample to make its value symmetric around zero. • Finally, the level-shifted values can be subjected to a forward point-wise intercomponent transformation to decorrelate the color data.

  5. Overview • DWT: • The DCT has been replaced by DWT, because the DWT has multi-resolution image representation inherent to it. The use of Integer DWT filters allows for both lossless and lossy compression within a single bit stream. • Quantizer: • Quantization is the element of lossy compression systems responsible for reducing the precision of data in order to make them more compressible. • Tier-1: • The arithmetic coding of the bitplane data is referred to as tier-1 (T1) coding. • Tier-2: • The compressed sub-bitplane coding passes can be aggregated into larger units named packets. This process of packetization along with its supporting syntax is often referred to as tier-2 (T2) coding.

  6. Progress & Goals • Progress: • 1. pre-processing • 2. DWT • Goals: • Minimum goal is to achieve the first 3 blocks, pre-processing, DWT and uniform quantizier with deadzone.

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