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In the emerging next generation video coding standard Versatile Video Coding (VVC) developed by the Joint Video Exploration Team (JVET), sub-block-based inter-prediction plays a key role in promising coding tools such as Affine Motion Compensation (AMC) and sub-block-based Temporal Motion Vector Prediction (sbTMVP). With sub-block-based inter-prediction, a coding block is divided into sub-blocks, and the motion information of each sub-block is derived individually. Although sub-block-based inter-prediction can provide a higher quality prediction benefiting from a finer motion granularity, it still suffers two problems: uneven prediction quality and boundary discontinuity. In this paper, we present a method of interweaved prediction to further improve sub-block-based inter-prediction. With interweaved prediction, a coding block with AMC or sbTMVP mode is divided into sub-blocks with two different dividing patterns, so that a corner position of a sub-block in one dividing pattern coincides with the central position of a sub-block in the other dividing pattern. Then two auxiliary predictions are generated by AMC or sbTMVP with the two dividing patterns, independently. The final prediction is calculated as a weighted-sum of the two auxiliary predictions. Theoretical analysis and statistical data prove that interweaved prediction can significantly mitigate the two problems in sub-block-based inter-prediction. Simulation results show that the proposed methods can achieve 0.64% BD-rate saving on average with the random access configurations. On sequences with rich affine motions, the average BD-rate saving can be up to 2.54%. [ABSTRACT FROM AUTHOR]

This paper proposes a deep learning method for intra prediction. Different from traditional methods utilizing some fixed rules, we propose using a fully connected network to learn an end-to-end mapping from neighboring reconstructed pixels to the current block. In the proposed method, the network is fed by multiple reference lines. Compared with traditional single line-based methods, more contextual information of the current block is utilized. For this reason, the proposed network has the potential to generate better prediction. In addition, the proposed network has good generalization ability on different bitrate settings. The model trained from a specified bitrate setting also works well on other bitrate settings. Experimental results demonstrate the effectiveness of the proposed method. When compared with high efficiency video coding reference software HM-16.9, our network can achieve an average of 3.4% bitrate saving. In particular, the average result of 4K sequences is 4.5% bitrate saving, where the maximum one is 7.4%. [ABSTRACT FROM AUTHOR]

We consider efficient image transmission via time-varying channels. To improve the performance, we propose a new distributed compressive sensing (CS) scheme that can leverage similar images in the cloud. It is featured by channel SNR and bandwidth scalability, high efficiency, and low encoding complexity. For each image, a compressed thumbnail is first transmitted after forward error correction (FEC) and modulation to retrieve similar images and generate a side information (SI) in the cloud. The residual image after subtracting the decompressed thumbnail is then coded and transmitted by CS through a very dense constellation without FEC. The linearly and ratelessly generated CS measurements make it capable of achieving both graceful quality degradation (GD) with the channel SNR and bandwidth scalability in a universal scheme. A mode decision and transform-domain power allocation are introduced for better bandwidth usage and protection against channel errors. At the decoder, a two-step CS decoding is performed to recover the residual signal, where both the local and nonlocal correlations within the image and that with the SI are exploited. Simulations on landmark images and an AWGN channel show that the received image quality gracefully increases with the channel SNR and bandwidth. Furthermore, it outperforms existing schemes both subjectively and objectively by up to 11 dB gains compared with the state-of-the-art transmission scheme with GD, i.e. SoftCast. [ABSTRACT FROM PUBLISHER]

Template matching (TM) was proposed in the literature a decade ago to efficiently remove non-local redundancies within an image without transmitting any overhead of displacement vectors. However, the large computational complexity introduced at both the encoder and the decoder, especially for a large search range, limits its widespread use. This paper proposes a hash-based line-by-line template matching (hLTM) for lossless screen image coding, where the non-local redundancy commonly exists in text and graphics parts. By hash-based search, it can largely reduce the search complexity of template matching without an accuracy degradation. Besides, the line-by-line template matching increases prediction accuracy by using a fine granularity. Experimental results show that the hLTM can significantly reduce both the encoding and decoding complexities by 68 and 23 times, respectively, compared with the traditional TM with a search radius of 128. Moreover, when compared with High Efficiency Video Coding screen content coding test model SCM-1.0, it can largely improve coding efficiency by up to 12.68% bits saving on screen contents with rich texts/graphics. [ABSTRACT FROM PUBLISHER]

With multimedia flourishing on the Web, it is easy to find similar images for a query, especially landmark images. Traditional image coding, such as JPEG, cannot exploit correlations with external images. Existing vision-based approaches are able to exploit such correlations by reconstructing from local descriptors but cannot ensure the pixel-level fidelity of the reconstruction. In this paper, a cloud-based distributed image coding (Cloud-DIC) scheme is proposed to exploit external correlations for mobile photo uploading. For each input image, a thumbnail is transmitted to retrieve correlated images and reconstruct it in the cloud by geometrical and illumination registrations. Such a reconstruction serves as the side information (SI) in the Cloud-DIC. The image is then compressed by a transform-domain syndrome coding to correct the disparity between the original image and the SI. Once a bitplane is received in the cloud, an iterative refinement process is performed between the final reconstruction and the SI. Moreover, a joint encoder/decoder mode decision at block, frequency, and bitplane levels is proposed to adapt to different correlations. Experimental results on a landmark image database show that the Cloud-DIC can largely enhance the coding efficiency both subjectively and objectively, with up to 5-dB gains and 70% bits saving over JPEG with arithmetic coding, and perform comparably at low bitrates with the intra coding of the High Efficiency Video Coding standard with a much lower encoder complexity. [ABSTRACT FROM AUTHOR]

Screen content like cartoons, captures of typical computer screens or video with text overlay or news ticker is an important category of video, which needs new techniques beyond the existing video coding techniques. In this paper, we analyze the characteristics of screen content and coding efficiency of HEVC on screen content. We propose a new coding scheme, which adopts a non-transform representation, separating screen content into color component and structure component. Based on the proposed representation, two coding modes are designed for screen content to exploit the directional correlation and non-translational changes in screen video sequences. The proposed scheme is then seamlessly incorporated into the HEVC structure and implemented into HEVC range extension reference software HM9.0. Experimental results show that the proposed scheme achieves up to 52.6% bitrate saving compared with HM9.0. On average, 35.1%, 29.2% and 23.6% bitrate saving are achieved with intra, random-access and low-delay configurations, respectively. The visual quality of the decoded video sequences is also significantly improved by reducing ringing artifacts around sharp edges and reserving the shape of text without blur. [ABSTRACT FROM PUBLISHER]

Computers are developing along with a new trend from the dual-core and quad-core processors to ones with tens or even hundreds of cores. Multimedia, as one of the most important applications in computers, has an urgent need to design parallel coding algorithms for compression. Taking intraframe/image coding as a start point, this paper proposes a pure line-by-line coding scheme (LBLC) to meet the need. In LBLC, an input image is processed line by line sequentially, and each line is divided into small fixed-length segments. The compression of all segments from prediction to entropy coding is completely independent and concurrent at many cores. Results on a general-purpose computer show that our scheme can get a 13.9 times speedup with 15 cores at the encoder and a 10.3 times speedup at the decoder. Ideally, such near-linear speeding relation with the number of cores can be kept for more than 100 cores. In addition to the high parallelism, the proposed scheme can perform comparatively or even better than the H.264 high profile above middle bit rates. At near-lossless coding, it outperforms H.264 more than 10 dB. At lossless coding, up to 14% bit-rate reduction is observed compared with H.264 lossless coding at the high 4:4:4 profile. [ABSTRACT FROM PUBLISHER]

While directional adaption is introduced into traditional transforms, different orders of two 1-D transforms will result in different results of one 2-D transform. Based upon an anisotropic image model, this paper analyzes the effect of transform orders in terms of theoretical coding gain. Our results reveal that the transform orders have little effect on the coding gain with full decomposition, good directional modes and good interpolation. However, in practical compression schemes, since high-pass bands are not decomposed fully because of the consideration on complexity, different transform orders have different coding performances, which can be solved by an adaptive transform order. Motivated by our analyzed results, a directional filtering transform (dFT, in order to distinguish from the common usage on DFT) is proposed in this paper to better exploit correlations among samples in H.264 intraframe coding. It provides an evenly distributed set of prediction modes with an adaptive transform order. Both interblock and intrablock correlations are exploited in this scheme. Experimental results in H.264 intraframe coding demonstrate its superiority both objectively and subjectively. [ABSTRACT FROM PUBLISHER]