Classifying videos into distinct categories, such as Sport and Music Video, is crucial for multimedia understanding and retrieval. Traditional methods require video decompression to extract pixel-level features like color, texture, and motion, thereby increasing computational and storage demands. We introduce a novel direction for video classification that does not rely on pixel domain information. Instead, we use the sequence of video frame sizes extracted from compressed bitstreams as input for a ResNet-based deep neural network, without the need for bitstream decoding or parsing. This approach leverages information captured by modern video compression algorithms, particularly advanced spatial and temporal prediction methods found in modern video coding standards such as H.264/AVC, H.265.HEVC and H.266/VVC.

Due to differences in frame structure, existing multi-rate video encoding algorithms cannot be directly adapted to encoders utilizing special reference frames such as AV1 without introducing substantial rate-distortion loss. To tackle this problem, we propose a novel bayesian block structure inference model inspired by a modification to an HEVC-based algorithm. It estimates the posterior probabilistic distributions of block partitioning, and adapts early terminations in the RDO procedure accordingly. Experimental results show that the proposed method provides flexibility for controlling the tradeoff between speed and coding efficiency, and can achieve an average time saving of 36.1% (up to 50.6%) with negligible bitrate cost.

Compared with conventional image and video, light field images introduce the weight channel, as well as the visual consistency of rendered view, information that has to be taken into account when compressing the pseudo-temporal-sequence (PTS) created from light field images. In this paper, we propose a novel frame level bit allocation framework for PTS coding. A joint model that measures weighted distortion and visual consistency, combined with an iterative encoding system, yields the optimal bit allocation for each frame by solving a convex optimization problem. Experimental results show that the proposed framework is effective in producing desired distortion distribution based on weights, and achieves up to 24.7% BD-rate reduction comparing to the default rate control algorithm.

In this article, we propose a probabilistic graphic model based fast HEVC encoding framework. A Bayesian network is characterized by the structure of the network (nodes and edges in the graph) and the probabilistic distributions. It can be constructed in three main steps: 1) data collection and pre-processing, 2) learning network structure, 3) learning parameters of the probabilistic distributions. In the first step, we select a subset of possible HEVC encoding parameters to be modeled by the Bayesian network. Data were collected using the first 150 frames of the HEVC common test condition Class D sequences. Then the model structure is trained so that it is consistent with the conditional dependencies from the observation. Finally, we use a Gaussian Bayesian network to properly model both discrete and continuous valued variables. Based on the Bayesian network and parameters, we can calculate the conditional probabilities for different status in the encoding process and neglect the events with probabilities smaller than a threshold. In this way, a fast HEVC encoding is achieved.

Summary form only given. To better exploit spatial correlations in a video frame, the HEVC video coding standard has introduced many intra prediction modes and a recursive quadtree-based coding unit (CU) structure. As a result, the complexity of Rate-distortion optimized (RDO) HEVC intra mode selection is significantly higher. Many techniques have been proposed to expedite the intra mode selection process to achieve a good overall trade-off between complexity and RD performance. In this paper, we proposed a fast intra decision algorithm based on Hadamard Transform. The algorithm consist of three parts: SATD calculation reduction, adaptive intra candidate selection, and SATD based early termination. Experiments conducted using the HEVC common test conditions show an average of 56.4% (up to 64.1%) time saving with only 1.2% increase in Bjontegaard delta rate (BD-rate) using the proposed algorithm.

The High Efficiency Video Coding (HEVC) standard achieves 50% improvement incompression efficiency over the widely used H.264/AVC standard at a cost of much higher complexity. The increase in complexity is due to, among other factors, the time needed to findthe optimal partition structure among the more flexible possibilities for the coding units (CUs) and prediction units (PUs). Many classification based algorithms have been proposed to reduce this partition decision time, but the features that can be acquired from current HEVC encoding order may not be sufficient to control the loss in coding efficiency. In this paper, we proposed an Early-Split (ES) order for HEVC encoding, where the encoder checks the split mode before the non-square PU partition modes and utilizes the encoding output of the subCUs to expedite subsequent encoding. Experiments show that the proposed algorithm achieved an average of 48% saving in encoding time with only 0.92% loss in the coding performance.

The existing intra flicker artifact reduction approaches, targeting at one of the major artifacts in current video encoding techniques, are not compatible with the distributed encoding structure, which is increasingly important in modern computing systems. To settle this problem, we propose a flicker reduction approach, which is effective, standard compliant, and especially suitable for parallel and distributed systems. Experimental results show that the proposed approach can reduce the flicker artifact by up to 60% on x265 and 14% on HM.

In this paper, we proposed a new rate control algorithm for High Efficiency Video Coding (HEVC), the latest video coding standard from the ITU/ISO. We use the information of pre-encoded 16x16 coding units (CUs) to estimate the characteristics of the largest coding unit (LCU). Based on the estimates, the proposed R -- λ model can be refined before the real encoding process. This is in contrast to rate control algorithms such as that in the HEVC reference software, where the model is updated based on a previously encoded picture. Experimental results show that the proposed rate control scheme can achieve accurate rate control with a BD-PSNR gain up to 5.37dB, compared to the state-of-the-art rate control algorithm in the HEVC test model (HM) 16.0. The largest PSNR improvement was over 6dB.

The emerging High Efficiency Video Coding (HEVC) standard achieves significant performance improvement over H.264/AVC standard at a cost of much higher complexity. In this paper, we propose a ultra fast H.264/AVC to HEVC transcoder for multi-core processors implementing Wave front Parallel Processing (WPP) and SIMD acceleration, along with expedited motion estimation (ME) and mode decision (MD) by utilizing information extracted from the input H.264/AVC stream. Experiments using standard HEVC test bit streams show that the proposed transcoder achieves 70x speed up over the HEVC HM 8.1 reference software (including H.264 encoding) at very small rate distortion (RD) performance loss.

In this paper, we present an improved algorithm for decoding live streamed or pre-encoded video bit streams with time-varying qualities. The algorithm extracts information available to the decoder from a high visual quality segment of the clip that has already been received and decoded, but was encoded independently from the current segment. The proposed decoder is capable of significantly improve the Quality of Experience of the user without incurring significant overhead to the storage and computational complexities of both the encoder and the decoder. We present simulation results using the HEVC reference encoder and standard test clips, and discuss areas of improvements to the algorithm and potential ways of incorporating the technique to a video streaming system or standards.

In this paper, we first analyze impact of the distribution of sparse signals on reconstruction quality in compressive sensing through experimental results and heuristic analysis. We suggest that trinary/binary sparse signals are one of the most difficult signals to reconstruct in terms of error bounds. We then show that by incorporating linear or non-linear mapping prior to sensing, significant improvement in the recovery performance can be achieved.

We describe an image coding algorithm combining the DCT and noiselet information. The algorithm first transmits DCT information sufficient to reproduce a "low-quality" version of the image at the decoder. This image is then used both at the decoder and encoder to create a mutually known list of locations of likely significant noiselet coefficients. The coefficient values themselves are then transmitted to the decoder differentially, by subtracting, at the encoder, the low-quality image from the original image, obtaining the noiselet values and subjecting them to quantization and entropy coding. There remain significant opportunities for further work combining CS-inspired information theoretic techniques with the rate-distortion considerations that are critical in practical image communications.

Macroblock level Horizontal Spatial Prediction(HSP) based intra frame coding scheme for High Dimension(HD) video sequences was proposed in this paper. According to the correlation experiment on HD sequences, most HD pictures have the stronger horizontal spatial correlation than the vertical spatial correlation, about 2dB stronger. This phenomena drop a valuable hint to us that the horizontal spatial prediction can be used in HD video intra coding without considering the vertical spatial prediction. An adaptive divide and predict intra frame coding scheme has been proposed by Piao which has the similar idea. But this method divided the whole picture into several parts which is not conform to the conventional macroblock based video coding framework and it has the high computation complexity in motion estimation procedure.

Summary form only given. This paper has described an improved algorithm for reconstructing sparse binary signals using compressive sensing. The algorithm is based on the reweighted lqnorm optimization algorithm, but with the important additional operation of bounding in each round of the interior-point method iteration, and progressive reduction of q. Experimental results confirm that the algorithm performs well both in terms of the ability to recover an input signal as well as in terms of speed. We also found that both the progressive reduction and the bounding are integral to the improvement in performance. Future work includes extending this approach to Gaussian distributed, as opposed to binary inputs.

In this paper, a fast RDO (rate-distortion optimization) quantization algorithm for H.264/AVC is proposed. In this algorithm, the searching space of level adjustments is reduced by filtering the input quantized coefficients in a hierarchical way. The well quantized coefficients is first filtered out, and then the RD tradeoff of each level adjustment to each of the rest coefficients is examined to select some good candidates with their associated level adjustments. Finally these good candidates are combined to find the best combination of level adjustments which gives the minimal rate-distortion cost. Furthermore, a fast rate estimation technique is adopted to save the rate-distortion estimation time. Experimental results show that about 44% quantization time on average can be saved at the cost of negligible PSNR loss compared with RDO quantization algorithm implemented in JM.

The paper described an optimal search based and a heuristic search based decoding algorithm for arithmetic codes and compared their performance and complexity with the traditional "hard" bit based AC decoder. To approach a different error resilient AC decoding, a heuristic search algorithms (HSAs) in artificial intelligence for finding the minimal-weight path through directed and nonnegatively-weighted graphs is utilized when the bitstream is transmitted over error prone channels. Simulation results showed that both algorithms easily outperformed traditional "hard" bits based arithmetic decoder, while the heuristic search based algorithm achieved a very good tradeoff between performance and complexity.

We present a method for utilizing soft information in decoding of variable length codes (VLCs). When compared with traditional VLC decoding, which is performed using "hard" input bits and a state machine, soft-input VLC decoding offers improved performance in terms of packet and symbol error rates. Soft-input VLC decoding is free from the risk, encountered in hard decision VLC decoders in noisy environments, of terminating the decoding in an unsynchronized state, and it offers the possibility to exploit a priori knowledge, if available, of the number of symbols contained in the packet.

The International Telecommunications Union (ITU) has adopted reversible variable length codes (RVLCs) for use in the emerging H.263+ video compression standard. As the name suggests, these codes can be decoded in two directions and can therefore be used by a decoder to enhance robustness in the presence of transmission bit errors. In addition, these RVLCs involve little or no efficiency loss relative to the corresponding non-reversible variable length codes. We present the ideas behind two general classes of RVLCs and discuss the results of applying these codes in the framework of the H.263+ and MPEG-4 video coding standards.