Lecture Digital image processing: Image compression - Nguyễn Công Phương

Lecture Digital image processing: Image compression presents the following content: Image compression – Decompression steps, error metrics, classifying image data, bit allocation, quantization, entropy coding, JPEG compression.
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Lecture Digital image processing: Image compression - Nguyễn Công Phương Nguyễn Công Phương DIGITAL IMAGE PROCESSING Image Compression Contents I. Introduction to Image Processing & Matlab II. Image Acquisition, Types, & File I/O III. Image Arithmetic IV. Affine & Logical Operations, Distortions, & Noise in Images V. Image Transform VI. Spatial & Frequency Domain Filter Design VII. Image Restoration & Blind Deconvolution VIII. Image Compression IX. Edge Detection X. Binary Image Processing XI. Image Encryption & Watermarking XII. Image Classification & Segmentation XIII. Image – Based Object Tracking XIV. Face Recognition XV. Soft Computing in Image Processing sites.google.com/site/ncpdhbkhn 2 Image Restoration and Blind Deconvolution 1. Introduction 2. Image Compression – Decompression Steps 3. Error Metrics 4. Classifying Image Data 5. Bit Allocation 6. Quantization 7. Entropy Coding 8. JPEG Compression sites.google.com/site/ncpdhbkhn 3 Introduction • Image compression: algorithmic techniques that can reduce the storage requirements of the image and, at the same time, retain the image information content. • Aspects of image properties that can be used for compression: – The interpixel information variation is only significant at edges of any type, whereas most of the image information content remains a slowly changing variable. – Our eyes are less sensitive to color changes and are much more sensitive to intensity. sites.google.com/site/ncpdhbkhn 4 Image Restoration and Blind Deconvolution 1. Introduction 2. Image Compression – Decompression Steps 3. Error Metrics 4. Classifying Image Data 5. Bit Allocation 6. Quantization 7. Entropy Coding 8. JPEG Compression sites.google.com/site/ncpdhbkhn 5 Image Compression Steps 1. Specification: This implies specifying the rate (bits available) and distortion (tolerable error) parameters for the target image. 2. Classification: This implies dividing the image data into various classes, based on their importance. Usually, some type of compression transform is utilized in this step to associate the important features with the most important class of information to be kept in the process of compression. 3. Bit allocation: This implies dividing the available bit budget among these classes such that the distortion is a minimum. 4. Quantization: This refers to quantizing each class separately using the bit allocation information derived in step 3. 5. Encoding: This corresponds to encoding each class separately using an entropy coder and write to the file. sites.google.com/site/ncpdhbkhn 6 Image Decompression Steps 1. Decoding: Read in the quantized data from the file, using an entropy decoder (reverse of step 5). 2. Dequantizing: This refers to normalizing the quantized values (reverse of steps 4 and 3). This also includes any padding or addition of missing values due to the quantization process. 3. Rebuilding: This involves the inverse transform or inverse classification of the normalized data into image pixels, essentially rebuilding the image (reverse of step 2). sites.google.com/site/ncpdhbkhn 7 Image Restoration and Blind Deconvolution 1. Introduction 2. Image Compression – Decompression Steps 3. Error Metrics 4. Classifying Image Data 5. Bit Allocation 6. Quantization 7. Entropy Coding 8. JPEG Compression sites.google.com/site/ncpdhbkhn 8 Error Metrics M N 1 MSE  M N  [ f (m, n)  g (m, n)] m 1 n 1 2  255  PSNR  20log10    MSE  • MSE (mean square error): a lower value for MSE means less error. • PSNR (peak signal-to-noise ratio): a higher value of PSNR is good. sites.google.com/site/ncpdhbkhn 9 Image Restoration and Blind Deconvolution 1. Introduction 2. Image Compression – Decompression Steps 3. Error Metrics 4. Classifying Image Data 5. Bit Allocation 6. Quantization 7. Entropy Coding 8. JPEG Compression sites.google.com/site/ncpdhbkhn 10 Classifying Image Data • In this step, usually the image is represented as a two-dimensional array of coefficients, where each coefficient represents the brightness level at that point. • From a high-level perspective, one cannot differentiate between coefficients as more important ones and lesser important ones, but … • Most natural images have smooth color variations, with the fine details being represented as sharp edges between the smooth variations. • Technically, the smooth variations in color can be termed low-frequency variations and the sharp variations, high-frequency variations. • The low-frequency components (smooth variations) constitute the base of an image, and the high-frequency components (the edges that give the detail) add upon them to refine the image, thereby giving a detailed image. • Hence, the smooth variations require more importance than the details. • Separating the smooth variations and details of the image can be done in many ways. Two well-known image transforms used for this purpose are the discrete cosine transform (DCT) and the discrete wavelet transform (DWT). sites.google.com/site/ncpdhbkhn ...