JPEG – 2000 COMPRESSION
JPEG – 2000 COMPRESSION Presented By
SOURYA PRAKASH PARIDA Roll no. EI 2001 17 202 Under the guidance of
DR. SAROJ KUMAR MEHER SOURYA PRAKASH PARIDA
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JPEG – 2000 COMPRESSION
Need for image compression Sufficient storage space. Lesser transmission time. Large transmission bandwidth. After transmission, the images can be decompressed at the receiver when required.
A compression ratio of N:1 reduces the space & transmission time by a factor of N as well as increases the transmission bandwidth by the same factor. SOURYA PRAKASH PARIDA
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JPEG – 2000 COMPRESSION
Principles behind compression Types of Redundancy:
Spatial Redundancy Spectral Redundancy Temporal Redundancy Fundamental Components of Compression:
Redundancy Reduction Irrelevancy Reduction
SOURYA PRAKASH PARIDA
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JPEG – 2000 COMPRESSION
Classification of compression Loss-less vs. Lossy Compression Loss-less: Digitally identical image after compression. Lossy: Greater image compression with blurred image output.
Predictive vs. Transform Coding Predictive: Information already received is used to predict future values. Transform: Signal is transformed from spatial domain to other space using a well-known transform.
Sub-band Coding The frequency band of a signal is split into various subbands, using octave tree decomposition of the image. SOURYA PRAKASH PARIDA
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JPEG – 2000 COMPRESSION
Image compression system
Source Encoder: Transforms the signal from the spatial domain to a well-known transform/domain.
Quantizer: Reduces the no. of bits needed to store the transformed coeff. by reducing the precision of those values.
Entropy Encoder: Further compresses the quantized values without any loss to give better overall compression. SOURYA PRAKASH PARIDA
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JPEG – 2000 COMPRESSION
DCT - based compression system
DCT- based JPEG Encoder Block Diagram
DCT- based JPEG Decoder Block Diagram SOURYA PRAKASH PARIDA
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JPEG – 2000 COMPRESSION
Disadvantages of DCT Undesirable blocking artifacts affect the reconstructed images or video frames.
Impossible to completely decorrelate the blocks at their boundaries using DCT.
Not efficient for binary image (fax or pictures of fingerprints) characterized by large periods of constant amplitude, followed by brief periods of sharp transitions. SOURYA PRAKASH PARIDA
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JPEG – 2000 COMPRESSION
Discrete Wavelet Transform (DWT) Wavelet transform decomposes a signal into a set of basis functions called wavelets.
SOURYA PRAKASH PARIDA
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JPEG – 2000 COMPRESSION
Advantages of DWT over DCT It provides higher compression ratios & avoids blocking artifacts.
Allows good localization both in spatial & frequency domain.
Transformation of the whole image introduces inherent scaling.
Better identification of which data is relevant to human perception higher compression ratio. SOURYA PRAKASH PARIDA
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JPEG – 2000 COMPRESSION
JPEG – 2000 Algorithm Division of the image into rectangular, non-overlapping tiles.
Tiling of components with different sub-sampling factors w.r.t. a high-resolution grid.
Maintaining the size of each tile to be the same, with the exception of tiles around the border (all four sides) of the image.
Conversion of the input series into high-pass & low-pass wavelet coefficient series (of length n/2 each) using DWT. SOURYA PRAKASH PARIDA
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JPEG – 2000 COMPRESSION
JPEG – 2000 Algorithm (contd.) The high-pass & low-pass wavelet coeff. series are given by:
k −1
H = ∑x i
m =0
2 i −m
k −1
L = ∑x i
m =0
SOURYA PRAKASH PARIDA
2 i −m
.s ( z ) m
.t ( z ) m
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JPEG – 2000 COMPRESSION
JPEG – 2000 Algorithm (contd.) Uniform scalar quantization of the wavelet coeff. employing a fixed dead-zone about the origin.
Division of the magnitude of each coeff. by a quantization step size and rounding down.
Division of each sub-band into regular non-overlapping rectangles by “packet partition”.
Three spatially consistent rectangles (one from each sub-band) comprise a packet partition location.
Code-blocks obtained by dividing each packet partition location into regular non-overlapping rectangles. SOURYA PRAKASH PARIDA
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JPEG – 2000 COMPRESSION
JPEG – 2000 Algorithm (contd.) Entropy coding carried out as context-dependent, binary, arithmetic coding of bitplanes.
Collection of each code-block in a packet partition location to form the body of a “packet.”
SOURYA PRAKASH PARIDA
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JPEG – 2000 COMPRESSION
Visual Comparison
Original Bitmap Image
JPEG (DCT) Image
JPEG-2000 (DWT) Image
(43:1 compression ratio)
(43:1 compression ratio)
The DWT-based JPEG-2000 image compression has a rate-distortion advantage over the original JPEG. SOURYA PRAKASH PARIDA
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JPEG – 2000 COMPRESSION
Conclusion JPEG – 2000 Provides
Protective image security. Improved low bit-rate compression performance. Transmission in noisy environments. Progressive transmission by pixel accuracy and resolution.
Improved continuous-tone and bi-level compression of larger images. SOURYA PRAKASH PARIDA
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