Quantization (image processing)

About: Quantization (image processing) is a research topic. Over the lifetime, 7977 publications have been published within this topic receiving 126632 citations.

Method of encoding image pixel values for storage as compressed digital data and method of decoding the compressed digital data

Abstract: A method of encoding an image for CD-I players comprises obtaining the pixel information as a first matrix (M1) of 768×560 pixel component values, decimation filtering (1) the first matrix (M1) to produce a second matrix (M2) of 384×560 pixel component values, encoding (2) the second matrix (M2) to produce a first set of DYUV digital data (RDD1) for storage on a compact disc (SM), applying the digital data (RDD1) to a decoder (3) to form a third matrix (M3) of 384×560 pixel component values, interpolation filtering (4) the third matrix (M3) to form a fourth matrix (M4) of 768×560 pixel component values, forming the difference (5) between the first (M1) and fourth (M4) matrices to produce a fifth matrix (M5) of 768×560 difference values and encoding the fifth matrix (M5) as respective multi-bit and/or run length codes (RDD1) for storage on a compact disc (SM). Compatibility with known hardware (the CD-I standard `basecase` player) is obtained by encoding negative difference values using the quantization levels in the guard range (0-15) below black level (16, on a scale of zero to 255).

Optimal design of JPEG hardware under the approximate computing paradigm

Abstract: JPEG compression based on the discrete cosine transform (DCT) is a key building block in low-power multimedia applications. We use approximate computing to exploit the error tolerance of JPEG and formulate a novel optimization problem that maximizes power savings under an error budget. We analyze the error propagation sensitivity in the DCT network and use this information to model the impact of introduced errors on the output quality. Simulations show up to 15% reduction in area and delay which corresponds to 40% power savings at iso-delay.

Analysis of Various Image Compression Techniques

Abstract: With the rapid development of digital technology in consumer electronics, the demand to preserve raw image data for further editing or repeated compression is increasing. Image compression is minimizing the size in bytes of an image without degrading the quality of the image to an unacceptable level. There are several different ways in which images can be compressed. This paper analyzes various image compression techniques. In addition, specific methods are presented illustrating the application of such techniques to the real-world images. We have presented various steps involved in the general procedure for compressing images. We provided the basics of image coding with a discussion of vector quantization and one of the main technique of wavelet compression under vector quantization. This analysis of various compression techniques provides knowledge in identifying the advantageous features and helps in choosing correct method for compression.

Wavelet-based image compression anti-forensics

Abstract: Because digital images can be modified with relative ease, considerable effort has been spent developing image forensic algorithms capable of tracing an image's processing history. In contrast to this, relatively little consideration has been given to anti-forensic operations designed to mislead forensic techniques. In this paper, we propose an anti-forensic technique capable of removing artifacts indicative of wavelet-based image compression from an image. Our technique operates by adding anti-forensic dither to a previously compressed image's wavelet coefficients so that the anti-forensically modified wavelet coefficient distribution matches a model of the coefficient distribution before compression. Simulation results show that our algorithm is capable of fooling current forensic image compression detection algorithms 100% of the time.