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.

A survey on compressed domain video analysis techniques

Abstract: Image and video analysis requires rich features that can characterize various aspects of visual information. These rich features are typically extracted from the pixel values of the images and videos, which require huge amount of computation and seldom useful for real-time analysis. On the contrary, the compressed domain analysis offers relevant information pertaining to the visual content in the form of transform coefficients, motion vectors, quantization steps, coded block patterns with minimal computational burden. The quantum of work done in compressed domain is relatively much less compared to pixel domain. This paper aims to survey various video analysis efforts published during the last decade across the spectrum of video compression standards. In this survey, we have included only the analysis part, excluding the processing aspect of compressed domain. This analysis spans through various computer vision applications such as moving object segmentation, human action recognition, indexing, retrieval, face detection, video classification and object tracking in compressed videos.

Adaptive approximate nearest neighbor search for fractal image compression

Abstract: Fractal image encoding is a computationally intensive method of compression due to its need to find the best match between image subblocks by repeatedly searching a large virtual codebook constructed from the image under compression. One of the most innovative and promising approaches to speed up the encoding is to convert the range-domain block matching problem to a nearest neighbor search problem. This paper presents an improved formulation of approximate nearest neighbor search based on orthogonal projection and pre-quantization of the fractal transform parameters. Furthermore, an optimal adaptive scheme is derived for the approximate search parameter to further enhance the performance of the new algorithm. Experimental results showed that our new technique is able to improve both the fidelity and compression ratio, while significantly reduce memory requirement and encoding time.

Overview and evaluation of the JPEG XT HDR image compression standard

Abstract: Standards play an important role in providing a common set of specifications and allowing inter-operability between devices and systems. Until recently, no standard for high-dynamic-range (HDR) image coding had been adopted by the market, and HDR imaging relies on proprietary and vendor-specific formats which are unsuitable for storage or exchange of such images. To resolve this situation, the JPEG Committee is developing a new coding standard called JPEG XT that is backward compatible to the popular JPEG compression, allowing it to be implemented using standard 8-bit JPEG coding hardware or software. In this paper, we present design principles and technical details of JPEG XT. It is based on a two-layer design, a base layer containing a low-dynamic-range image accessible to legacy implementations, and an extension layer providing the full dynamic range. The paper introduces three of currently defined profiles in JPEG XT, each constraining the common decoder architecture to a subset of allowable configurations. We assess the coding efficiency of each profile extensively through subjective assessments, using 24 naive subjects to evaluate 20 images, and objective evaluations, using 106 images with five different tone-mapping operators and at 100 different bit rates. The objective results (based on benchmarking with subjective scores) demonstrate that JPEG XT can encode HDR images at bit rates varying from 1.1 to 1.9 bit/pixel for estimated mean opinion score (MOS) values above 4.5 out of 5, which is considered as fully transparent in many applications. This corresponds to 23-times bitstream reduction compared to lossless OpenEXR PIZ compression.

Further study on YASS: steganography based on randomized embedding to resist blind steganalysis

Abstract: We present further extensions of yet another steganographic scheme (YASS), a method based on embedding data in randomized locations so as to resist blind steganalysis. YASS is a JPEG steganographic technique that hides data in the discrete cosing transform (DCT) coefficients of randomly chosen image blocks. Continuing to focus on JPEG image steganography, we present, in this paper, a further study on YASS with the goal of improving the rate of embedding. Following are the two main improvements presented in this paper: (i) a method that randomizes the quantization matrix used on the transform domain coefficients, and (ii) an iterative hiding method that utilizes the fact that the JPEG "attack" that causes errors in the hidden bits is actually known to the encoder. We show that using both these approaches, the embedding rate can be increased while maintaining the same level of undetectability (as the original YASS scheme). Moreover, for the same embedding rate, the proposed steganographic schemes are more undetectable than the popular matrix embedding based F5 scheme, using features proposed by Pevny and Fridrich for blind steganalysis.

RD-OPT: an efficient algorithm for optimizing DCT quantization tables

Abstract: The Discrete Cosine Transform (DCT) is widely used in lossy image and video compression schemes such as JPEG and MPEG. In this paper we describe RD-OPT, an efficient algorithm for constructing DCT quantization tables with optimal rate-distortion tradeoffs for a given image. The algorithm uses DCT coefficient distribution statistics in a novel way and uses a dynamic programming strategy to produce optimal quantization tables over a wide range of rates and distortions. It can be used to compress images at any desired signal-to-noise ratio or compressed size.