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This paper describes signal processing in the encrypteddomain, i.e., that after encryption but before decryption. In this framework, signal processing operations can be directly applied to encrypted signals without decrypting of encrypted signals, whereas the ordinary framework encrypts signals for transmission and/or storing but it decrypts them before signal processing operations are applied to. The described framework befits contemporary cloud computing in which not only transmission but also storing and processing are done in the public Internet. Addition to brief survey, two tangible application scenarios are also demonstrated in this paper where a new signal processing algorithm is introduced each.

This research presents an image compression algorithm using modified Haar wavelet and vector quantization. For comparison purposes, a standard Haar wavelet with vector quantization and SPIHT, which is used in JPEG2000, are compared with the proposed method using Peak Signal-to-Noise Ratio (PSNR). The proposed method shows better results on average over the compared methods.

A system is described, which is based on structural and perceptual redundancy. It employs the idle periods of one picture to introduce information from other pictures and integrates over several TV frames. The system is capable of multiplexing 15 high-resolution black and white TV channels.

Data compression techniques are classified into four categories which describe the effect a compression method has on the form of the signal transmitted. Each category is defined and examples of techniques in each category are given. Compression methods which have received previous investigation, such as the geometric aperture methods, as well as techniques which have not received much attention, such as Fourier filter, optimum discrete filter, and variable sampling rate compression, are described. Results of computer simulations with real data are presented for each method in terms of rms and peak errors versus compression ratio. It is shown that, in general, the geometric aperture techniques give results comparable to or better than the more "exotic" methods and are more economical to implement at the present state-of-the-art. In addition, the aperture compression methods provide bounded peak error which is not readily obtainable with other methods. A general system design is given for a stored-logic data compression system with adaptive buffer control to prevent loss of data and to provide efficient transmission of multiplexed channels with compressed data. An adaptive buffer design is described which is shown to be practical, based on computer simulations with five different types of representative data.

The increased complexity in spacecraft systems and experiment packages has resulted in large quantities of data to be processed and transmitted to ground stations, often in remote locations. The automatic removal of redundant information before transmission would allow more efficient use of the communication link, as well as yield a reduction in the net amount of information to be processed on the ground. This paper describes a proposed self-adaptive approach to data compression, and a method of extending the approach to a multichannel data compression system.

A new video digital data compression technique, time-buffered coarse-fine (TBCF), is presented along with experimental results. This technique has the ability to produce high-quality video pictures with compression ratios, referenced to a 6-bit pulse code modulation (PCM), from 2-to-1 to greater than 20-to-1 depending on the subject complexity and the element accuracy required, with a 6-to-1 compression typical. This system uses a modified version of the coarse-fine compressor invented and implemented by the authors with additional processing of the data by noise recognition and removal circuitry and run-length coding. The coarse-fine system is briefly discussed followed by a detailed description of the time-buffered coarse-fine system operation. Time-buffered coarse-fine compressed pictures are then presented and analyzed.

A hardware-implemented algorithm combining dynamic thresholding and edge detection allows black-white representation of gray-scale images for computer storage. The output resembles a highly detailed sketch, with resolution and contrast superior to a halftone representation. The same algorithm can be applied to text and line drawings, so that all three types of images can be stored using the same compression algorithm.

This bibliography lists published papers that specifically relate to the compression or quality of digitized images of graphics data. By graphics data we mean printed matter, handwriting, line drawings, or any image data that is basically two-level: information on background. The data compression covered here assumes information reduction to images with two levels of signal amplitude followed by redundancy reduction of these binary (i.e., black/white) images. Quality measurements covered are confined to the two-level data resulting from the previous information reduction step for graphics data.

Long before the development of electrical systems and devices, optical systems were being used to perform the col-lection, transmission and storage functions of information processing. In recent years, the mathematical structure common to electrical signal analysis and optics has led to increased interaction between the two disciplines, with tremendous benefit to both. Optics has provided insight into the field of image processing by digital and electrical techniques, including image transmission and coding, image enhancement and restoration, and pattern recognition.1-8 At the same time, the analysis and synthesis of optical systems and advances in microscopy, spectroscopy and interferometry have directly benefited from the insights of signal and communication theory mathematics.9-13

Data compression and error control have, over the years, been treated as two separate disciplines. Data compression can substantially reduce the loading of communication channels and error control using coding methodology, can reduce the amount of errors in the messages being transmitted, or allow the system to operate with less power for a comparable uncoded information rate. This paper demonstrates that both functions can be combined into one operation by applying sequential decoding developed for error control to data compression. Because the same general method can be used to solve both problems, data compression and error control can be united in a single system and held accountable for the required theorems in information theory.

Spacecraft imaging systems produce amounts of data which can easily exceed the down-link channel capacity. This paper demonstrates data compression methods which reduce significantly the communication requirements with minimum loss of information in the received imagery. Differential PCM data compression takes advantage of the lower entropy of the scene-invariant brightness difference signal. Entropy coding of the compressor output provides maximum signal-to-noise ratio per channel bit. Two-bit-per-sample imagery is shown to be equivalent to 6-bit PCM. Reconstruction error and subjective evaluation of actual images are used to measure compression performance.

This paper deals with the compression of the data obtained from measurements in real-tome non-linear estimation problems. The purpose is to reduce the data processing requirements. Since data compression is never optimal, one always can expect a certain deterioration in the performance of the estimator when using the compressed data. However, it is shown theoretically that under certain conditions one can preprocess the raw measurement data such that the loss of information (in the sense of Fisher) is minimized. In this case, the estimation performance is practically the same as when no data compression is utilized. A specific application, the tracking of a reentry vehicle with a linearized filter, is discussed in more detail and simulation results are presented. The conditions derived theoretically are satisfied in this case and the results confirm this.

Recent work has stimulated considerable interest in using radar pulse compression concepts to improve the quality of medical X-ray and gamma ray photographs. (Refs. 1 and 2) These techniques involve using a coded point spread function to obtain increased resolution and reduced ex posure time. The coded point spread function allows use of a larger aperture in the X-ray camera, but requires a post-detection processing step in order to decode the data and obtain the desired image. The point spread function must be coded in such a anner that its two-dimensional autocorrelation function is very sharply peaked. In the post-detection pro cessing; the coded data are cross-correlated with the chosen point spread function to obtain the desired image.

An optimum compression ratio (OCR) is defined for a source, leading to "efficiency" of a compression algorithm and resulting in a good evaluation of data-compression schemes. Applications to first- and second-order Markov sources, in conjunction with the "fan" and "zero-order hold" compression schemes, are also presented.