In the context of low-cost video encoding, distributed video coding (DVC) has recently emerged as a potential candidate for uplink-oriented applications. This paper builds on a concept of correlation channel (CC) modeling, which expresses the correlation noise as being statistically dependent on the side information (SI). Compared with classical side-information-independent (SII) noise modeling adopted in current DVC solutions, it is theoretically proven that side-information-dependent (SID) modeling improves the Wyner-Ziv coding performance. Anchored in this finding, this paper proposes a novel algorithm for online estimation of the SID CC parameters based on already decoded information. The proposed algorithm enables bit-plane-by-bit-plane successive refinement of the channel estimation leading to progressively improved accuracy. Additionally, the proposed algorithm is included in a novel DVC architecture that employs a competitive hash-based motion estimation technique to generate high-quality SI at the decoder. Experimental results corroborate our theoretical gains and validate the accuracy of the channel estimation algorithm. The performance assessment of the proposed architecture shows remarkable and consistent coding gains over a germane group of state-of-the-art distributed and standard video codecs, even under strenuous conditions, i.e., large groups of pictures and highly irregular motion content.

In video coding systems using adaptive arithmetic coding to compress texture information, the employed symbol probability models need to be retrained every time the coding process moves into an area with different texture. To avoid this inefficiency, we propose to replace the probability models used in the original coder with multiple switchable sets of probability models. We determine the model set to use in each spatial region in an optimal manner, taking into account the additional signaling overhead. Experimental results show that this approach, when applied to H.264/AVC's context-based adaptive binary arithmetic coder (CABAC), yields significant bit-rate savings, which are comparable to or higher than those obtained using alternative improvements to CABAC previously proposed in the literature.

Triggered by the challenging prerequisites of wireless capsule endoscopic video technology, this paper presents a novel distributed video coding (DVC) scheme, which employs an original hash-based side-information creation method at the decoder. In contrast to existing DVC schemes, the proposed codec generates high quality side-information at the decoder, even under the strenuous motion conditions encountered in endoscopic video. Performance evaluation using broad endoscopic video material shows that the proposed approach brings notable and consistent compression gains over various state-of-the-art video codecs at the additional benefit of vastly reduced encoding complexity.

In the current development of HEVC, compression performance improved significantly compared to H.264/AVC for both inter pictures and intra pictures. With intra compression, the main reason for this improvement is the large in crease in intra prediction directions (up to 34). The downside of having a larger number of modes is that they increase the signaling overhead in the bitstream. In this paper, a low complexity intra mode prediction algorithm is proposed which improves the mode prediction accuracy. This is achieved by exploiting the correlation between the prediction directions of the neighboring prediction units and that of the encoded prediction unit. As a result, more efficient intra mode signaling can be achieved with minimal impact on encoder and decoder complexity. On average, 0.33% bitrate improvement is obtained by employing the proposed algorithm. For sequences that are encoded with a high number of directional intra modes, around 1% bitrate improvement is measured.

Error resilience is of paramount importance in video transmission over variable-bandwidth error-prone channels, such as wireless channels. In this paper, we investigate the influence of corrupted motion vectors in video coding based on motion compensated temporal filtering, and develop various error resilience and concealment mechanisms for this class of codecs. The experimental results show that our proposed motion vector coding technique significantly increases the robustness against transmission errors at the cost of less than 3% in terms of rate. It is also shown that our proposed spatial error-concealment mechanism leads to performance gains of up to 6 dB in comparison to a classical slicing-based approach employing no error concealment.

Recently proposed scalable wavelet-based video codecs using spatial-domain motion compensated temporal filtering (SDMCTF) offer competitive compression performance when compared to H.264 and generate embedded bit-streams supporting quality, resolution and temporal scalability. To be able to support a large range of bit-rates with optimal compression efficiency, these codecs require a quality-scalable motion vector coding technique. Such an algorithm based on the integer wavelet transform followed by embedded coding of the wavelet coefficients was proposed in the recent past. In this paper, we present a quality-scalable motion vector coding algorithm using median-based motion vector prediction. The compression performance of the proposed algorithm is compared to that of the wavelet-based technique and is found to be superior. Additionally, the proposed motion vector codec is incorporated into an SDMCTF-based video codec and the benefits of using quality-scalable motion vector representations are experimentally demonstrated.

An enhanced, off-line, segment-based rate control approach is proposed for controlling the distortion variation across successive segments of a video sequence when encoding with single-layer (MPEG-4 Baseline, MPEG-4 AVC) and wavelet video codecs. Consistent quality is achieved by a time-efficient, predictive rate-distortion modeling per segment. The individual segments are either shots or subshots, that are defined based on shot segmentation and activity analysis techniques. The rate control method solves the quality stability problem of state-of-the-art codecs, especially for less mature rate control modules (such as present in MPEG-4 AVC). The processing overhead compared to classical two-pass VBR encoding is limited while the distortion variation is significantly reduced.

Video coding techniques yielding state-of-the-art compression performance require large amount of computational resources, hence practical implementations, which target a broad market, often tend to trade-off coding efficiency and flexibility for reduced complexity. Scalable video coding instead, not only provides seamless adaptation to bit-rate variation, but also allows the end user to trim down the resources he needs to perform real-time decoding by limiting the process to a subset of the original content. Hence, by choosing the quality, frame-rate and/or resolution of the reconstructed sequence, each decoder can meet its hardware limitations without affecting the encoding process of the media provider. This paper proposes a preliminary analysis of the memory-access behavior of a fully scalable video decoder and investigates the capability of selecting the operational settings in order to adapt to the available hardware resources on the target device.

This paper presents a novel fully-scalable wavelet video coding scheme that performs efficient open-loop motion compensated temporal filtering (MCTF) in the wavelet domain (in-band). Unlike the conventional spatial-domain MCTF (SDMCTF) schemes, which apply MCTF on the original image data and then encode the residual image using a critically-sampled wavelet transform, the framework presented here applies the in-band MCTF (IBMCTF) after the discrete wavelet transform (DWT) is performed in the spatial dimensions. To overcome the inefficiency of motion estimation (ME) in the wavelet domain, a complete-to-overcomplete DWT (CODWT) is performed. The proposed framework provides improved quality (SNR) and temporal scalability as compared with existing in-band closed-loop temporal prediction schemes with ODWT and improved spatial scalability as compared to SDMCTF. We present a thorough comparison between SDMCTF and the proposed IBMCTF in terms of coding efficiency and scalability. Furthermore, we describe several extensions that enable the filtering of the various bands to be performed independently, based on the resolution, sequence content, complexity requirements and desired scalability.

Recently, a new wavelet-based video codec using in-band motion compensated temporal filtering (IBMCTF) was introduced This codec is fully scalable in resolution, quality aid frame-rate. In comparison to an equivalent video coding scheme based on spatial domain motion compensated temporal filtering (SDMCTF), its compression performance when decoding to lower resolutions is very promising. However, since the IBMCTF scheme is based on in-band motion estimation, considerably more motion vector data is generated than in the SDMCTF scheme. Efficient compression of these motion vectors is therefore of utmost importance. In this paper, several solutions for the compression of motion vectors generated by a video codec based on IBMCTF are presented and compared.

Several techniques based on the three-dimensional (3-D) discrete cosine transform (DCT) have been proposed for volumetric data coding. These techniques fail to provide lossless coding coupled with quality and resolution scalability, which is a significant drawback for medical applications. This paper gives an overview of several state-of-the-art 3-D wavelet coders that do meet these requirements and proposes new compression methods exploiting the quadtree and block-based coding concepts, layered zero-coding principles, and context-based arithmetic coding. Additionally, a new 3-D DCT-based coding scheme is designed and used for benchmarking. The proposed wavelet-based coding algorithms produce embedded data streams that can be decoded up to the lossless level and support the desired set of functionality constraints. Moreover, objective and subjective quality evaluation on various medical volumetric datasets shows that the proposed algorithms provide competitive lossy and lossless compression results when compared with the state-of-the-art.