Recent developments have given birth to H.264/AVC: a video coding standard offering better bandwidth to video quality ratios than previous standards (such as H.263, MPEG-2, MPEG-4, etc.), due to its improved inter- and intraprediction modes at the expense of higher computation complexity. It is expected that H.264/AVC will take over the digital video market, replacing the use of previous standards in most digital video applications. This creates an important need for heterogeneous video transcoding technologies from older standards to H.264. In this paper, we focus our attention on the interframe prediction, the most computationally intensive task involved in the heterogeneous video transcoding process. This paper presents a novel macroblock (MB) mode decision algorithm for interframe prediction based on data mining techniques to be used as part of a very low complexity heterogeneous video transcoder. The proposed approach is based on the hypothesis that MB coding mode decisions in H.264 video have a correlation with the distribution of the motion compensated residual in the decoded video. We use data mining tools to exploit the correlation and derive decision trees to classify the incoming decoded MBs into one of the several coding modes in H.264. The proposed approach reduces the H.264 MB mode computation process into a decision tree lookup with very low complexity. For general validation purposes, we apply our algorithm to two of the most important heterogeneous video transcoders: MPEG-2 to H.264 and H.263 to H.264. Our results show that the our data-mining based transcoding algorithm is able to maintain a good video quality while considerably reducing the computational complexity by 72% on average when applied in MPEG-2 to H.264 transcoders, and by 62% on average when applied in H.263 to H.264 transcoders. Finally, we conduct a comparative study with some of the most prominent fast interprediction methods for H.264 presented in the literature. Our results show that the proposed data mining-based approach achieves the best results for video transcoding applications.

In this paper, we propose the use of data mining algorithms to create a macroblock partition mode decision algorithm for inter-frame prediction, to be used as part of a high-efficient H.263 to H.264 transcoder. We use machine learning tools to exploit the correlation and derive decision trees to classify the incoming H.263 MC residual into one of the several coding modes in H.264. The proposed approach reduces the H.264 MB mode computation process into a decision tree lookup with very low complexity. Experimental results show that the proposed approach reduces the inter-prediction complexity by as much as 60% while maintaining the coding efficiency.

The estimation of the video quality is often performed using a full reference approach. One of the most important steps in a video quality measurement algorithm is to find the corresponding frames between the reference and the distorted video sequence. In this paper an algorithm with three steps is proposed. First, an extended version of the phase correlation is used to find candidate images with an arbitrary temporal offset, spatial scaling or spatial shift. Based on the assumption that the spatial scaling and spatial shift does not change during the sequence a set of probable parameters is selected. Finally, a maximum likelihood estimation is applied to select those temporal offsets which support the smoothest playback. A set of video sequences degraded with several distortions which are typical for multimedia scenarios are used to compare the performance to other algorithms.

The quality of coded images is often assessed by a subjective test. Usually the viewers get as much time as they need to find a stable result. In video sequences however, the viewer has to judge the quality in a shorter time that is defined by the changing content or a following scene cut. Therefore it is desirable to know the influence of a shorter presentation time on the perceptibility of distortions. In this paper we present the results of a suitable subjective test on coded still images. The images were presented for six different durations, ranging from 200 ms to 3 s. Special care was taken to avoid the memorization effect usually present after short presentations. The results show that the viewers tend to avoid extreme votings at short durations. The variance of the votings is also discussed in detail. Based on the result of the voting for the longest presentation time, we propose a prediction model for the voting of the shorter durations using a logistic curve fit. This presentation time model (PTM) is presented and analysed in detail.

The presented work addresses the reduction of computational complexity for transcoding of interframes from H.264 to H.263 baseline profiles maintaining the quality of a full search approach. This scenario aims to achieve fast backward compatible interoperability inbetween new and existing video coding platforms, e.g. between DVB-H and UMTS. By exploiting side information of the H.264 input bitstream the encoding complexity of the motion estimation is strongly reduced. Due to the possibility to divide a macroblock (MB) into partitions with different motion vectors (MV), one single MV has to be selected for H.263. It will be shown, that this vector is suboptimal for all sequences, even if all existing MVs of a MB of H.264 are compared as candidate. Also motion vector refinement with a fixed ½-pel refinement window as used by transcoders throughout the literature is not sufficient for scenes with fast movement. We propose an algorithm for selecting a suitable vector candidate from the input bitstream and this MV is then refined using an adaptive window. Using this technique, the complexity is still low at nearly optimum rate-distortion results compared to an exhaustive full-search approach.

With the standardization of H.264/AVC by ITU-T and ISO/IEC and the adaptatation into new hardware, the necessity of transcoding between existing standards and H.264 will arise to achieve interoperability between hardware devices. Because of the many new prediction parameters as well as the pixel-based deblocking filter and the new transform of H.264 this is a difficult task to perform. In our work we propose a fast cascaded pixel-domain transcoder from H.263 to H.264 for both intra- and inter-frame coding. The rate-distortion (RD) performance of the encoded bitstreams is compared to an exhaustive full-search approach. Our approach leads to 9% higher data rate in average, but the computational complexity for the prediction can be reduced by 90% and more. It will be shown that the algorithms proposed for H.263 are applicable for transcoding MPEG-2 to H.264, too

Transcoding is a technique to convert one video bit-stream into another. While homogeneous transcoding is done at the same coding standard, inhomogeneous transcoding converts from one standard format to another standard. Inhomogeneous transcoding between MPEG-2, MPEG-4 or H.263 was performed using the same transform. With the standardisation of H.264 also a new transform basis and different block size was defined. For requantization from block size 8times8 to 4times4 this leads to the effect that the quantization error of one coefficient in a block of size 8times8 is distributed over multiple coefficients in blocks of size 4times4. In our work, we analyze the requantization process for inhomogeneous transcoding with different transforms. The deduced equations result in an expression for the correlation of the error contributions from the coefficients of block size 8times8 at each coefficient of block size 4times4. We then compare the mathematical analysis to simulations on real sequences. The reference to the requantization process is the direct quantization of the undistorted signal. It will be shown that the loss is as high as 3 dB PSNR at equivalent step size for input and output bitstream. Also an equation for the choice of the second quantization step size in dependency of the requantization loss is deduced. The model is then extended from the DCT to the integer-based transform as defined in H.264