Compound Coding
This work explores the advancements in compound coding, focusing on classification algorithms that distinguish between text and graphics data. We analyze past contributions in spatial and DCT-based methods, alongside wavelet-based algorithms. Our DCT codebook method utilizes distinctive distributions of DCT coefficient values for varied regions. We present a comparison of classification algorithms, including smoothing techniques. Our findings indicate that text and graphics possess unique characteristics, with our methods achieving efficient coding at 81 bits/pel for compound coding compared to 83 bits/pel for uniform coding.
Compound Coding
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Presentation Transcript
Compound Coding Mark Kalman Isaac Keslassy Daniel Wang 12/6/00
Outline • Motivating Compound Coding • Classification Algorithms • Work from the Literature • Spatial • DCT-Based Methods • Wavelets • Our DCT Codebook Method • Comparison • Smoothing • An Example
At 1bit/pel: Median graphics PSNR = 31dB Median text PSNR = 19.5 dB Different properties => different coding Motivating Compound Coding
Past Work (1) • Spatial Domain Algorithms • Chen (1990) : block variance • Bones et al (1990) : edges (Sobel filter) • DCT-Based Algorithms • Chaddha et al (1995) : DCT-18 Absolute-Sum • Konstantinides (2000) : DCT Bit Rate
Past Work (2) • Wavelet-based Algorithms • c2goodness of fit to Laplacian • similar to MSE • “discreteness” of distribution • concentration of data in peaks
Our DCT Codebook Scheme • DCT coefficient values are differently distributed in text and graphic regions • Each distribution is given by a codebook histogram graphics text Distribution for DCT coefficient (2,1)
Smoothing Classification Algorithm Non-linear Smoothing Linear Smoothing
An Example Uniform Coding .83 bits/pel Compound Coding .81 bits/pel
Conclusion • Text and graphics have different characteristics • Many methods of classifying blocks • Spatial, DCT, Wavelets, DCT Codebooks • Smoothing/post-processing • Our methods compare favorably