Bit plane coding
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This document discusses techniques for image compression including bit-plane coding, bit-plane decomposition, constant area coding, and run-length coding. It explains that bit-plane decomposition represents a grayscale image as a collection of binary images based on its representation as a binary polynomial. Run-length coding compresses each row of a binary image by coding contiguous runs of 0s or 1s with their length, separately for black and white runs. Constant area coding classifies blocks of pixels as all white, all black, or mixed and codes them with special codewords.
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notes_Image Compression.pptHarisMasood20 Digital image compression techniques aim to reduce the number of bits required to represent an image by minimizing redundancy. There are two main categories: lossless compression preserves all image information, while lossy compression provides higher data reduction but less than perfect image reproduction. Common methods include removing coding, interpixel, and psychovisual redundancies through techniques like variable-length coding, transform coding, and quantization.
notes_Image Compression_edited.ppt
notes_Image Compression_edited.pptHarisMasood20 Digital image compression techniques aim to reduce the number of bits required to represent an image by minimizing redundancy. There are two main categories: lossless compression preserves all image information while lossy compression provides higher data reduction but less than perfect image reproduction. Common methods include removing coding, interpixel, and psychovisual redundancies through techniques like variable length coding, discrete cosine transform, and quantization.
Performance Analysis of Compression Techniques Using SVD, BTC, DCT and GP
Performance Analysis of Compression Techniques Using SVD, BTC, DCT and GPIOSR Journals This document summarizes and compares four different image compression techniques: Singular Value Decomposition (SVD), Block Truncation Coding (BTC), Discrete Cosine Transform (DCT), and Gaussian Pyramid (GP). It analyzes the performance of each technique based on metrics like Peak Signal to Noise Ratio (PSNR), Mean Squared Error (MSE), and compression ratio. The techniques are tested on biometric images from iris, fingerprint, and palm print databases to evaluate image quality after compression.
B017120611
B017120611IOSR Journals This document summarizes and compares four different image compression techniques: Singular Value Decomposition (SVD), Block Truncation Coding (BTC), Discrete Cosine Transform (DCT), and Gaussian Pyramid (GP). It analyzes the performance of each technique based on metrics like Peak Signal to Noise Ratio (PSNR), Mean Squared Error (MSE), and compression ratio. Experiments were conducted on biometric image databases to compress and reconstruct images using these four techniques, and the results were evaluated and compared based on the above-mentioned metrics.
Cj36511514
Cj36511514IJERA Editor 1. The document presents an image segmentation algorithm that uses local thresholding in the YCbCr color space.
2. It computes local thresholds for each pixel by calculating the mean and standard deviation of neighboring pixels in a 3x3 mask. The threshold is used to label each pixel as 1 or 0.
3. The algorithm was tested on images with objects indistinct and distinct from the background. It performed well in segmenting objects from the background in both cases. There is potential to improve performance for blurred images.
Image compression and it’s security1
Image compression and it’s security1Reyad Hossain The document discusses image compression techniques. It outlines the fundamentals of image compression including encoding, decoding, and algorithms like JPEG and JPEG 2000. Image compression aims to reduce data storage and transmission requirements by removing redundant pixel information. It allows for more efficient sharing and storage of images across industries like printing, data storage, telecommunications, satellite imaging, and television.
Jpeg image compression using discrete cosine transform a survey
Jpeg image compression using discrete cosine transform a surveyIJCSES Journal Due to the increasing requirements for transmission of images in computer, mobile environments, the
research in the field of image compression has increased significantly. Image compression plays a crucial
role in digital image processing, it is also very important for efficient transmission and storage of images.
When we compute the number of bits per image resulting from typical sampling rates and quantization
methods, we find that Image compression is needed. Therefore development of efficient techniques for
image compression has become necessary .This paper is a survey for lossy image compression using
Discrete Cosine Transform, it covers JPEG compression algorithm which is used for full-colour still image
applications and describes all the components of it.
Comparison between JPEG(DCT) and JPEG 2000(DWT) compression standards
Comparison between JPEG(DCT) and JPEG 2000(DWT) compression standardsRishab2612 This topic comes under the Image Processing.In this comparison between JPEG and JPEG 2000 compression standard techniques is made.The PPT comprises of results, analysis and conclusion along with the relevant outputs
06 cie552 image_manipulation
06 cie552 image_manipulationElsayed Hemayed The document discusses several image manipulation techniques:
1) Color quantization reduces the number of colors in an image using algorithms like uniform quantization, popularity algorithm, and median cut algorithm.
2) Image halftoning converts a full color image to a bi-level or multi-level image to be displayed on devices with limited color capabilities using dithering patterns.
3) Digital watermarking embeds copyright information into images invisibly using techniques like least significant bit insertion in the spatial domain.
Image compression 14_04_2020 (1)
Image compression 14_04_2020 (1)Joel P Digital image processing involves compressing images to reduce file sizes. Image compression removes redundant data using three main techniques: coding redundancy reduction assigns shorter codes to more common pixel values; spatial and temporal redundancy reduction exploits correlations between neighboring pixel values; and irrelevant information removal discards visually unimportant data. Compression is achieved by an encoder that applies these techniques, while a decoder reconstructs the image for viewing. Popular compression methods include Huffman coding and arithmetic coding. Compression allows storage and transmission of images and video using less data while maintaining acceptable visual quality.
Image pcm 09
Image pcm 09anilbl This paper proposes a new image compression approach that uses adaptive DCT-domain downsampling to reduce high frequency information in images for compression, and then uses learning-based mapping to compensate for the removed high frequencies during decompression. Specifically, it adaptively selects regions for downsampling in the DCT domain based on rate-distortion optimization. It then uses a database of visual patterns to map blurred image patches to corresponding high-quality patches during decompression, recovering lost high frequencies. Experimental results show it outperforms standards like H.264 and JPEG2000 especially at low bit rates.
Ec section 
Ec section Antriksh Saxena This document discusses digital image processing using MATLAB. It begins by defining digital images and how they are represented by arrays of pixels in computer memory. It then discusses how images can be read into MATLAB and converted between color, grayscale, and binary representations. Various image processing operations are described such as edge detection, dilation, filling, and calculating region properties. Finally, examples are given of processing color images using intensity transformations and gamma correction.
Image processing
Image processingAntriksh Saxena This document discusses digital image processing using MATLAB. It begins by defining digital images and how they are represented by arrays of pixels in computer memory. It then discusses how images can be read into MATLAB and converted between color, grayscale, and binary representations. Various image processing operations are described such as morphology, edge detection, dilation, filling, and filtering. Region properties like centroids and bounding boxes are also extracted. Finally, intensity adjustments on RGB images using imadjust are demonstrated.
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