Image deletion refers to removing images from a compressed image set in cloud servers, which has always received much attention. However, in some cases images are not successfully deleted, and coding performance still...
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Image deletion refers to removing images from a compressed image set in cloud servers, which has always received much attention. However, in some cases images are not successfully deleted, and coding performance still remains to rise. In this paper, we propose a low-complexity and high-coding-efficiency image deletion algorithm. First, all the images are classified into to-be-deleted images, images unneeded to be processed, and images needed to be processed further divided into images needed to be only decoded and images needed to be re-encoded. Then, we also propose a depth- and subtree-constrained minimum spanning tree (DSCMST) heuristics to produce the DSCMST of images needed to be processed. Third, every image unneeded to be processed is added to the just obtained DSCMST as the child of the vertex that is still its parent in the compressed image set. Finally, after the encoding of images needed to be re-encoded, a new compressed image set is constructed, implying the completion of image deletion. Experimental results show that under various circumstances our proposed algorithm can effectively remove any images, including root vertex, internal vertices, and leaf vertices. Moreover, compared with state-of-the-art methods, the proposed algorithm achieves higher codingefficiency while having the minimum complexity.
An H.264 video encoder adopts multiple encoding tools to achieve high coding efficiency at the expense of high computational complexity. The allowable computational complexity for real-time video encoding, however, is...
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An H.264 video encoder adopts multiple encoding tools to achieve high coding efficiency at the expense of high computational complexity. The allowable computational complexity for real-time video encoding, however, is generally limited in a wireless handset. This research proposes a complexity control mechanism that is composed of two algorithms to minimise the distortion of each encoded video frame under the computational complexity constraint and the rate constraint. The first proposed algorithm performs optimal complexity allocation among encoding tools based on a new complexity-rate-distortion (C-R-D) model. This model precisely describes how each encoding tool influences the C-R-D performance of the encoder with concise formulas. Accordingly, the algorithm obtains the optimal complexity of each encoding tool by a closed-form solution with small complexity overhead. Based on a new C-D model of motion estimation, this work proposes the second algorithm that performs optimal complexity allocation among macro-blocks to further allocate suitable complexity to each macro-block. Experiments performed on a software-optimised source code show that these two algorithms yield superior performance to the existing algorithms.
A new algorithm for achieving flexible tiling, of the time axis for audio coding purposes is presented, It is based on the calculus of the distances among a predetermined number of time-frequency pairs, From the compu...
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A new algorithm for achieving flexible tiling, of the time axis for audio coding purposes is presented, It is based on the calculus of the distances among a predetermined number of time-frequency pairs, From the computed distances. a clustering process determines the final subdivision of each audio frame. Experimental results demonstrates the good performance of the proposed algorithm. which provides highcoding, efficiency with a reduced complexity.
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