Rate-distortion methods for image and video compression

Ortega, Antonio; Ramchandran, Kannan

doi:10.1109/79.733495

Cited by 687 publications

(407 citation statements)

References 93 publications

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“…As an alternative to blocks, depth can also be estimated using meshes [11,12] or pixel-wise regularization [7]. However, in such methods the image encoder and the depth encoder operate at different RD slopes, which penalizes the overall codec efficiency and makes it difficult to optimally allocate the bitrate [13].…”

Section: Introductionmentioning

confidence: 99%

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Wavelet-Based Joint Estimation and Encoding of Depth-Image-Based Representations for Free-Viewpoint Rendering

Maitre

Shinagawa²,

Minh

2008

IEEE Trans. on Image Process.

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Abstract-We propose a wavelet-based codec for the static Depth-Image-Based Representation (DIBR), which allows viewers to freely choose the viewpoint. The proposed codec jointly estimates and encodes the unknown depth map from multiple views using a novel Rate-Distortion (RD) optimization scheme. The rate constraint reduces the ambiguity of depth estimation by favoring piecewise-smooth depth maps. The optimization is efficiently solved by a novel dynamic programming along trees of integer wavelet coefficients. The codec encodes the image and the depth map jointly to decrease their redundancy and to provide a RD-optimized bitrate allocation between the two. The codec also offers scalability both in resolution and in quality. Experiments on real data show the effectiveness of the proposed codec.

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Section: Introductionmentioning

confidence: 99%

“…The parameter b of the Laplace distribution is estimated using bracketing and a search akin to bisection [13,28]. A large bracket is initially chosen, whose size is iteratively reduced.…”

Section: F Bitrate Optimizationmentioning

confidence: 99%

Wavelet-Based Joint Estimation and Encoding of Depth-Image-Based Representations for Free-Viewpoint Rendering

Maitre

Shinagawa²,

Minh

2008

IEEE Trans. on Image Process.

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“…Furthermore, a rate-distortion optimization process is performed that allows the encoder to choose the best coding option among different combinations of the previously described tools [48], [49]. In this paper the number of tools designed and implemented in our encoder is limited.…”

Section: B Discussionmentioning

confidence: 99%

Directional Transforms for Video Coding Based on Lifting on Graphs

Martinez-Enriquez

Cid-Sueiro

Díaz-de-María

et al. 2018

IEEE Trans. Circuits Syst. Video Technol.

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Abstract-In this work we describe and optimize a general scheme based on lifting transforms on graphs for video coding. A graph is constructed to represent the video signal. Each pixel becomes a node in the graph and links between nodes represent similarity between them. Therefore, spatial neighbors and temporal motion-related pixels can be linked, while nonsimilar pixels (e.g., pixels across an edge) may not be. Then, a lifting-based transform, in which filterin operations are performed using linked nodes, is applied to this graph, leading to a 3-dimensional (spatio-temporal) directional transform which can be viewed as an extension of wavelet transforms for video. The design of the proposed scheme requires four main steps: (i) graph construction, (ii) graph splitting, (iii) filte design, and (iv) extension of the transform to different levels of decomposition. We focus on the optimization of these steps in order to obtain an effective transform for video coding. Furthermore, based on this scheme, we propose a coefficien reordering method and an entropy coder leading to a complete video encoder that achieves better coding performance than a motion compensated temporal filterin wavelet-based encoder and a simple encoder derived from H.264/AVC that makes use of similar tools as our proposed encoder (reference software JM15.1 configu ed to use 1 reference frame, no subpixel motion estimation, 16 × 16 inter and 4 × 4 intra modes).

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“…The Rate Distortion(R-D) theory is a classical framework for bit allocation [2]. For arbitrary set of quantizers optimal solution of bit allocation has been defined by Shoham and Gersho [3].…”

Section: Introductionmentioning

confidence: 99%

Block Classification and Transmission of Compressed Image with Bit Allocation and Unequal Error Protection

Patel¹,

Patnaik²,

Mansi³

2016

IJCTE

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Abstract-In image/video transmission system block or macro block classification has frequently been used to classify similar types of properties of block like spectral, statistical, perceptual etc. The bit allocation algorithm distribute total number of bits among a finite set of quantizer to maximize the quality. This paper proposed joint source channel coding (JSCC) algorithm for image transmission with: (i) optimum bit allocation for classified blocks (ii) Unequal Error Protection (UEP) at channel coding stage to minimize the total distortion. The bit allocation applied at two levels. First distribute allocate source rate between three different classes and second each class individual transforms coefficient bit assignment. The simulation results shows that using this algorithm approximately 3-4 dB improvement compare to conventional UEP and further 1.5-2 dB improvement using optimum bit allocation algorithm.

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Rate-distortion methods for image and video compression

Cited by 687 publications

References 93 publications

Wavelet-Based Joint Estimation and Encoding of Depth-Image-Based Representations for Free-Viewpoint Rendering

Wavelet-Based Joint Estimation and Encoding of Depth-Image-Based Representations for Free-Viewpoint Rendering

Directional Transforms for Video Coding Based on Lifting on Graphs

Block Classification and Transmission of Compressed Image with Bit Allocation and Unequal Error Protection

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