Angular intraprediction (AIP) is a coding tool that has been incorporated into the video coding standards H.264/AVC (Advanced Audio Coding) and High Efficient Video Coding. In this paper, we study how the efficiency of AIP depends on its prediction parameters. To carry out this paper, we first theoretically analyze the variance of the error incurred when a perfectly directional signal is predicted in a certain direction. The results of this analysis are then used to study the efficiency of AIP when it is applied to a distribution of directions. To facilitate mathematical derivations, we make several assumptions about the signal and the prediction process, and we use some approximations. This allows us to obtain simple expressions for the variance of the AIP prediction error as a function of signal and prediction parameters. Finally, we compare our theoretical results with the results obtained from the prediction of images containing rectilinear edges. This comparison shows that our theoretical expressions follow the main trends of the experimental results except when AIP is performed with a very high accuracy.

Pulse-code modulation (PCM) with embedded quantization allows the rate of the PCM bitstream to be reduced by simply removing a fixed number of least significant bits from each codeword. Although this source coding technique is extremely simple, it has a poor coding efficiency. In this paper, we present a generalized PCM (GPCM) algorithm for images that simply removes bits from each codeword. In contrast to PCM, however, the number and the specific bits that a GPCM encoder removes in each codeword depends on its position in the bitstream and the statistics of the image. Since GPCM allows the encoding to be performed with different degrees of computational complexity, it can adapt to the computational resources that are available in each application. Experimental results show that GPCM outperforms PCM with a gain that depends on the rate, the computational complexity of the encoding, and the degree of inter-pixel correlation of the image.

Some video applications require the use of extremely simple source coding techniques. For these applications, Modulo-PCM is an interesting alternative to PCM since it has a very low complexity and performs better than PCM. In this paper, we present an adaptive Modulo-PCM algorithm for image coding. Our algorithm divides the image into blocks and performs a proper Modulo-PCM coding of each block. Since our algorithm allows different degrees of encoding complexity, it can adapt to the computational resources that are available in each video application. Experimental results show that our algorithm improves the coding efficiency of both non-adaptive MPCM and PCM. The magnitude of the improvements depends on the encoding complexity and the target rate.

In this paper, we propose a new Distributed Video Coding (DVC) algorithm based on Compressive Sampling principles. Our encoding algorithm transmits a set of measurements of every frame block. Using these measurements, the decoder finds an approximation of each block as a linear combination of a small number of blocks in previously transmitted frames. Thanks to the simplicity of the encoding, our algorithm can be useful in those video applications that require very low complex encoders. However, our algorithm is less efficient than another state-of-the-art DVC technique.

In this paper, we present a low complexity Modulo-PCM based coding algorithm for high speed video cameras used in applications that demand very high frame rates. By compressing the raw digital video generated at the camera front-end, our algorithm reduces the high data transfer speed required in this type of cameras. At the encoder, our algorithm compress the raw digital video by selectively removing bits. At the decoder, the spatial correlation between pixels is taken into account in order to achieve an efficient decoding. Experimental results show that our algorithm achieves significant improvements with respect to the simple strategy of dropping the same number of bits in all the pixels when high rate reductions are necessary or when the spatial correlation of the frames is high.

In some video coding applications, it is desirable to reduce the complexity of the video encoder at the expense of a more complex decoder. Distributed video (DV) coding is a new paradigm that aims to achieve this. To allocate a proper number of bits to each frame, most DV coding algorithms use a feedback channel (FBC). However, in some cases, a FBC does not exist. In this paper, we therefore propose a rate allocation (RA) algorithm for pixel-domain distributed video coders without FBC. Our algorithm estimates at the encoder the number of bits for every frame without significantly increasing the encoder complexity. Experimental results show that our RA algorithm delivers satisfactory estimates of the adequate encoding rate, especially for sequences with little motion.

Generally speaking, rate scalable video systems today are evaluated operationally, meaning that the algorithm is implemented and the rate-distortion performance is evaluated for an example set of inputs. However, in these cases it is difficult to separate the artifacts caused by the compression algorithm and data set with general trends associated with scalability. In this paper, we derive and evaluate theoretical rate-distortion performance bounds for both layered and continuously rate scalable video compression algorithms which use a single motion-compensated prediction (MCP) loop. These bounds are derived using rate-distortion theory based on an optimum mean-square error (MSE) quantizer, and are thus applicable to all methods of intraframe encoding which use MSE as a distortion measure. By specifying translatory motion and using an approximation of the predicted error frame power spectral density, it is possible to derive parametric versions of the rate-distortion functions which are based solely on the input power spectral density and the accuracy of the motion-compensated prediction. The theory is applicable to systems which allow prediction drift, such as the data-partitioning and SNR-scalability schemes in MPEG-2, as well as those with zero prediction drift such as fine granularity scalability MPEG-4. For systems which allow prediction drift we show that optimum motion compensation is a sufficient condition for stability of the decoding system.

In this paper, we analyze the efficiency of three signal-to-noise scalable strategies for video coders using single-loop motion-compensated prediction (MCP). In our analysis, we assume the video sequences have uniform and constant translational motion and we model MCP as a stochastic filter. We also assume an exponential model for the distortion-rate function of the intraframe coding. The analysis is divided into two parts: the steady-state analysis and the transient analysis. In the first part, only the steady-state response of the coders is taken into account, and, thus, this analysis allows us to asses approximately the efficiency of coders with long input sequences. The transitory analysis considers both the transient and the steady-state responses of the coders, which makes it appropriate to analyze coders using periodic intraframes or with short input sequences. To validate our analysis, theoretical results have been compared to results from encodings of real video sequences using the scalable adaptive motion compensated wavelet video coder. We show that our theoretical analysis effectively describes qualitatively the main trends of every video coding strategy.

In SNR-scalable motion compensated video coders, prediction drift is introduced when decoding below the rate at which the encoder loop operates. Prediction drift reduces the coding efficiency and changes the quality of the video over time which is very annoying visually. In this paper, we propose a rate allocation algorithm to reduce these quality variations. By modeling both the video signal and the coder, we analyze the efficiency of the coder and find the rate allocation which creates frames which have the same distortion when decoded. We have compared numerical simulations of our algorithm with experimental results from real video encodings. The results show that by using a proper rate allocation algorithm quality fluctuations can be reduced.

In this paper, we present two rate allocation algorithms for embedded motion compensated video coders. The algorithms are based on the modeling of both the video signal and the coder which allow us to express the coding distortion with a recurrence equation. Our algorithms assign rates to the frames of each group of pictures (GOP) of a video sequence in an optimum way. In the first algorithm, the criterion is to minimize the average (MENAVE) distortion and in the second to achieve constant distortion (CD) in all frames. Numerical simulations show the MENAVE criterion can introduce large variations in quality with no significant gains in average distortion with respect to the CD criterion. We also show how the motion estimation accuracy and the GOP length influence in both strategies.

In this paper, we derive and evaluate theoretical rate-distortion performance bounds for scalable video compression algorithms which use a single motion-compensated prediction (MCP) loop. These bounds are derived using rate-distortion theory based on an optimum mean-square error (MSE) quantizer. By specifying translatory motion and using an approximation of the predicted error frame power spectral density, it is possible to derive parametric versions of the rate-distortion functions which are based solely on the input power spectral density and the accuracy of the motion-compensated prediction. The theory is applicable to systems which allow prediction drift, such as the SNR-scalability in MPEG-2, as well as those with zero prediction drift such as the MPEG-4 fine grained scalable standard.

Unlike conventional layered scalable video coding, leaky prediction layered video coding (LPLC) introduces a leaky factor /spl alpha/, which takes on values in the range between 0 and 1, to partially include the enhancement layer in the motion compensation loop, hence obtaining a trade-off between coding efficiency and error resilience performance. In this paper, we use quantization noise modeling to theoretically analyze the rate distortion performance of LPLC. An alternative block diagram of LPLC is first developed, which significantly simplifies the theoretical analysis. Closed form expressions, as a function of the leaky factor, are derived for two scenarios, where drift error occurs in the enhancement layer and no drift occurs within the motion compensation loop. Theoretical results are evaluated with respect to the leaky factor, showing that a leaky factor of 0.4-0.6 is a good choice in terms of the overall rate distortion performance of LPLC.

In this paper, an analysis of the efficiency of three signal-to-noise ratio (SNR) scalable strategies for motion compensated video coders and their non-scalable counterpart is presented. After assuming some models and hypotheses with respect to the signals and systems involved, we have obtained the SNR of each coding strategy as a function of the decoding rate. To validate our analysis, we have compared our theoretical results with data from encodings of real video sequences. Results show that our analysis describes qualitatively the performance of each scalable strategy, and therefore, it can be useful to understand main features of each scalable technique and what factors influence their efficiency.

In this paper, we present an analysis of the performance of three predictive fine granular SNR-scalable coders and compare them with their non-scalable version. In our study, we assume an exponential model for the quantization noise and the use of linear prediction. Coders efficiency is assessed through the signal-to-noise ratio as a function of rate (SNR(R)) and the mean SNR. Validity of our analysis is tested by comparing theoretical results with simulations of the encoding of realizations of first order autoregressive processes. Results show that the use of coders which tolerate some prediction drift provides better results than other conventional scalable schemes.

We present a new algorithm for lossless compression of bilevel images. The algorithm represents the image to be compressed with a sequential logic function and then, minimizes it to obtain a more compact representation. As the minimization of Boolean functions requires a high computational cost, our algorithm represents them with ordered binary-decision diagrams (OBDD), which can be simplified with a low computational cost. Once the OBDD of the sequential logic function has been obtained and reduced, it is coded efficiently in order to reduce the redundancy present on it. Arithmetic coding is also used to remove even more the residual redundancy. The results obtained show significant improvements with respect to previous works.