Neeraj Gadgil

The use of asymmetric numeral systems as an accurate replacement for Huffman coding

Entropy coding is an integral part of most data compression systems. Huffman coding (HC) and arithmetic coding (AC) are two of the most widely used coding methods. HC can process a large symbol alphabet at each step allowing for fast encoding and decoding. However, HC typically provides suboptimal data rates due to its inherent approximation of symbol probabilities to powers of 1 over 2. In contrast, AC uses nearly accurate symbol probabilities, hence generally providing better compression ratios. However, AC relies on relatively slow arithmetic operations making the implementation computationally demanding. In this paper we discuss asymmetric numeral systems (ANS) as a new approach to entropy coding. While maintaining theoretical connections with AC, the proposed ANS-based coding can be implemented with much less computational complexity. While AC operates on a state defined by two numbers specifying a range, an ANS-based coder operates on a state defined by a single natural number such that the x ∈ ℕ state contains ≈ log2(x) bits of information. This property allows to have the entire behavior for a large alphabet summarized in the form of a relatively small table (e.g. a few kilobytes for a 256 size alphabet). The proposed approach can be interpreted as an equivalent to adding fractional bits to a Huffman coder to combine the speed of HC and the accuracy offered by AC. Additionally, ANS can simultaneously encrypt a message encoded this way. Experimental results demonstrate effectiveness of the proposed entropy coder.

Chapter 8 – Multiple Description Coding *

Abstract With the development of 3G/4G and WiFi networks, there has been a growing demand for multimedia delivery over wireless channels. The dramatic increase in multimedia traffic over such lossy channels has driven the development of efficient, reliable, and adaptable coding techniques. Multiple description coding (MDC) has emerged as one of the most effective coding techniques to address such scenarios, especially in case of real-time applications when retransmission is unacceptable. In MDC, source data is coded into multiple descriptions containing controlled redundancy that is used to combat the unpredictable packet loss during delivery across a channel. Using the received descriptions, the source data is reconstructed by the decoder. Since its conception, many MDC based tools and systems have been developed for generic and specific applications. In this chapter, we review the development of MDC in the context of some selected applications.

Nuclei segmentation of fluorescence microscopy images based on midpoint analysis and marked point process

Microscope image analysis is challenging because of non-uniform brightness, decreasing image contrast with tissue depth, poor edge details and irregular and unknown structures. In this paper, we present a nuclei segmentation and counting method using midpoint analysis, shape-based function optimization and a MPP simulation with a spatial birth-death process. Preliminary results demonstrate efficacy of the proposed method.

A web-based video annotation system for crowdsourcing surveillance videos

Video surveillance systems are of a great value to prevent threats and identify/investigate criminal activities. Manual analysis of a huge amount of video data from several cameras over a long period of time often becomes impracticable. The use of automatic detection methods can be challenging when the video contains many objects with complex motion and occlusions. Crowdsourcing has been proposed as an effective method for utilizing human intelligence to perform several tasks. Our system provides a platform for the annotation of surveillance video in an organized and controlled way. One can monitor a surveillance system using a set of tools such as training modules, roles and labels, task management. This system can be used in a real-time streaming mode to detect any potential threats or as an investigative tool to analyze past events. Annotators can annotate video contents assigned to them for suspicious activity or criminal acts. First responders are then able to view the collective annotations and receive email alerts about a newly reported incident. They can also keep track of the annotators’ training performance, manage their activities and reward their success. By providing this system, the process of video analysis is made more efficient.

Adaptive error concealment for temporal-spatial multiple description video coding

With the development of 3G/4G and WiFi networks, there has been a growing demand for video delivery over wireless channels. This increase of video traffic creates a significant challenge for efficient, reliable and adaptable video coding techniques. Multiple description coding (MDC) is an error resilient video coding method that has been proposed for streaming applications when retransmission is unacceptable. In this paper, we present error concealment approaches for two different four-description MDC architectures. In our first architecture, four descriptions are generated by temporal-spatial subsampling and encoded independently. To improve the error robustness, several adaptive error concealment methods are described. One of them is a frame-level method based on error tracking. It takes into account the distortion due to error concealment and error propagation. The other methods are macroblock-level methods based on foreground-background mapping and distortion mapping respectively. Our second MDC partition architecture aims at better coding efficiency and improved error robustness. Here spatial partitioning is done after prediction so that each of the two descriptions shares the same prediction loop. Experimental results are presented to demonstrate the efficacy of our proposed methods. HighlightsA new MDC is proposed for better coding efficiency and error robustness.A four-description MDC based on temporal and spatial splitting is described.Adaptive error concealment methods are used to combat packet loss.Experimental results demonstrate the efficacy of the proposed MDC methods.

Spatial subsampling-based multiple description video coding with adaptive temporal-spatial error concealment

The encoded bitstream of a typical video compression method is vulnerable to errors during transmission over lossy network. Multiple Description Coding (MDC) is an error resilient video coding suitable for real-time applications when retransmission is unacceptable. Subsampling-based MDC with error concealment is often effective for mitigating unpredictable losses. In this paper, a spatial subsampling based MDC with an adaptive concealment method is described. Error concealment is done using the motion information from its spatial-temporal neighbors. Preliminary experimental results exhibit the efficacy of the proposed method.

An HEVC compressed domain content-based video signature for copy detection and video retrieval

Video sharing platforms and social networks have been growing very rapidly for the past few years. The rapid increase in the amount of video content introduces many challenges in terms of copyright violation detection and video search and retrieval. Generating and matching content-based video signatures, or fingerprints, is an effective method to detect copies or “near-duplicate” videos. Video signatures should be robust to changes in the video features used to characterize the signature caused by common signal processing operations. Recent work has focused on generating video signatures based on the uncompressed domain. However, decompression is a computationally intensive operation. In large video databases, it becomes advantageous to create robust signatures directly from the compressed domain. The High Efficiency Video Coding (HEVC) standard has been recently ratified as the latest video coding standard and wide spread adoption is anticipated. We propose a method in which a content-based video signature is generated directly from the HEVC-coded bitstream. Motion vectors from the HEVC-coded bitstream are used as the features. A robust hashing function based on projection on random matrices is used to generate the hashing bits. A sequence of these bits serves as the signature for the video. Our experimental results show that our proposed method generates a signature robust to common signal processing techniques such as resolution scaling, brightness scaling and compression.

Image-Like 2D Barcodes Using Generalizations of the Kuznetsov–Tsybakov Problem

In this paper, we propose a novel method for generating visually appealing two-dimensional (2D) barcodes that resemble meaningful images to human observers. The technology of 2D barcodes, currently dominated by quick response codes, is widely adopted in many applications, including product tracking, document management, and general marketing. Such barcodes typically lack user friendly appearance and do not convey any visual significance to human observers. The proposed method addresses this problem by allowing 2D barcodes to resemble an arbitrary image or a logo. Our method is based on a generalization of the Kuznetsov-Tsybakov problem that served as a foundation for wet paper codes, commonly adopted in digital steganography. We introduce weaker statistical constraints to obtain additional flexibility allowing the barcode to assume the appearance of an arbitrary pattern. This paper provides the theoretical analysis of the proposed coding framework and a practical algorithm for rapid approximation of the optimal code. We also discuss the introduction of error correction capabilities, and experimentally evaluate a prototype implementation in a smartphone-based acquisition scenario.

Adaptive error concealment for Multiple Description Video Coding using motion vector analysis

Multiple Description Coding (MDC) is an efficient error resilient video coding method especially for real-time applications when retransmission is unacceptable. For applications with scalable, multicast and P2P environments, it is advantageous to use more than two descriptions. In this paper, we present an adaptive temporal-spatial error concealment method using motion vector analysis to improve the performance of MDC. Experimental results demonstrate the efficacy of the proposed method.

Generalizations of the Kuznetsov-Tsybakov problem for generating image-like 2D barcodes

Many two-dimensional (2D) barcodes, such as quick response (QR) codes, lack user-friendly appearance. Our goal in this paper is to generate 2D barcodes that “look” like recognizable images or logos. Standard steganographic methods hide a message (payload) in an image usually by modifying bits in a specific way using predetermined pixels of the image. This approach cannot be directly used for very low bit rates commonly used in 1 bit per pixel 2D barcodes. It is possible to produce barcodes in which the grayness of a pixel in an image is interpreted as the probability of assigning a value (black or white) to the corresponding pixel of the encoded message (payload). This can be viewed as statistical constraints enforced on the encoded bit-sequence. Using an information theoretic approach, Kuznetsov and Tsybakov have shown that this can be done for a specific case of constraints almost without any loss of capacity. In this paper, we propose generalizations of this approach with weaker constraints as an application to generating 2D barcodes that resemble images. We describe a coding framework, various types of constraints, a practical approximation and some example 2D barcodes generated from our implementation.