17-11-2012, 06:25 PM
Video/Image Compression Technologies
VideoImage Compression.pdf (Size: 163.91 KB / Downloads: 38)
Introduction
– Impact of Digitization, Sampling and Quantization on Compression
Lossless Compression
– Bit Plane Coding
– Predictive Coding
Lossy Compression
– Transform Coding (MPEG-X)
– Vector Quantization (VQ)
– Subband Coding (Wavelets)
– Fractals
– Model-Based Coding
Digitization Impact
Generating Large number of bits; impacts storage and transmission
Image/video is correlated Human Visual System has limitations
Types of Redundancies
Spatial - Correlation between neighboring pixel values
Spectral - Correlation between different color planes or spectral bands
Temporal - Correlation between different frames in a video sequence
Know Facts
Sampling
Higher sampling rate results in higher pixel-to-pixel correlation
Quantization
Increasing the number of quantization levels reduces pixel-to-pixel correlation
Lossless Compression
Lossless
Numerically identical to the original content on a pixel-by-pixel basis
Motion Compensation is not used
Applications
Medical Imaging
Contribution video applications
Techniques
Bit Plane Coding
Lossless Predictive Coding
DPCM, Huffman Coding of Differential Frames, Arithmetic Coding of Differential
Transform Coding
Desirable characteristics:
Content decorrelation: packing the most amount of energy in the fewest number of coefficients
Content-Independent basis functions
Fast implementation
Available transformations:
Karhunen-Loeve Transform (KLT)
Basis functions are content-dependent
Computationally complex
Discrete Fourier Transform (DFT/FFT)
Real and Imaginary components (Amplitude and Phase)
Fast Algorithms
Discrete Cosine Transform (DCT)
Real transformation
Fast algorithm
Best energy packing property
Walsh-Hadamard Transform (WHT)
Poor energy packing property
Simple hardware implementation, low-cost and fast
Vector Quantization
– Codebook Generation
Best results are obtained when the codebook is generated from the content itself (Local
codebooks)
• Computationally intensive task
• Creates overhead - codebook has to be transmitted to the receiver as overhead
Global Codebooks
• Linde-Buzo-Gray (LBG) clustering algorithm
– Training content from the same class of content is used
– The larger the codebook the higher the bit rate, the higher the quality of the content